NBA EDA Project

The EDA project in this course has four main parts to it:
1. Project Proposal 2. Phase 1 3. Phase 2 4. Report This notebook will be used for Project Proposal, Phase 1, and Phase 2. You will have specific questions to answer within this notebook for Project Proposal and Phase 1. You will also continue using this notebook for Phase 2. However, guidance and expectations can be found on Canvas for that assignment. The report is completed outside of this notebook (delivered as a PDF). Detailed instructions for that assignment are provided in Canvas.

Read this before proceeding: 1. Review the list of data sets and sources of data to avoid before choosing your data. This list is provided in the instructions for the Project Proposal assignment in Canvas.

2. It is expected that when you are asked questions requiring typed explanations you are to use a markdown cell to type your answers neatly. Do not provide typed answers to questions as extra comments within your code. Only provide comments within your code as you normally would, i.e. as needed to explain or remind yourself what each part of the code is doing.

Project Proposal

The intent of this assignment is for you to share your chosen data file(s) with your instructor and provide general information on your goals for the EDA project.

Step 1 (2 pts): Give a brief description of the source(s) of your data and include a direct link to your data.

I am using data can be found at https://www.basketball-reference.com/leagues/NBA_2020_totals.html

The data contains individual players statistics in the NBA. It has 30 different attributes to measure overall players statistics for all the regular NBA seasons from the year 2015 to 2020.

Step 2 (2 pts): Briefly explain why you chose this data.

I have been watching NBA Basketball games since childhood. A huge fan of Chicago Bulls team, which led to interest of choosing the overall NBA Players Statistics data. My initial intuition is that in past few years NBA players have been scoring more points in 3-pointers shooting categories and are more likely to be in teams that make the NBA playoffs and win the Larry O'Brien Championship Trophy.

Step 3 (1 pt): Provide a brief overview of your goals for this project.

The goal of this project is to confirm my hunch is either correct or incorrect regarding shooting 3-pointers is a more optimal solution compared to 2-pointers. I suspect that based on my initial intuition NBA players scoring more in 3-pointers offensive categories are more likely to lead points total and be in teams that make NBA playoffs. To answer my hypothetical question we need to analyze the data from the 2015-2020 NBA seasons, and further research into other attributes of the player's statistics such as Position, Team, Games, Minutes Played, and Field Goal Percentages. As the most current NBA Player Statistics Data does not contain the Playoffs information I will be creating reference data of teams that reached the playoffs each year to merge with the current dataset.

Step 4 (1 pt): Read the data into this notebook.

import pandas as pd

from IPython.core.interactiveshell import InteractiveShell
InteractiveShell.ast_node_interactivity = "all"

nba_stats_season1 = pd.read_csv('nba_stats_2015_2016.csv')

Step 5 (1 pt): Inspect the data using the info( ), head( ), and tail( ) methods.

# TODO: Use the info() method to determine to inspect the variable (column) names, the number of non-null values,
#       and the data types for each variable.

# TODO: Use the head() method to inspect the first five (or more) rows of the data

# TODO: Use the tail() method to inspect the last five (or more) rows of the data
nba_stats_season1.info()
nba_stats_season1.head()
nba_stats_season1.tail()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 578 entries, 0 to 577
Data columns (total 30 columns):
 #   Column  Non-Null Count  Dtype  
---  ------  --------------  -----  
 0   Rk      578 non-null    int64  
 1   Player  578 non-null    object 
 2   Pos     578 non-null    object 
 3   Age     578 non-null    int64  
 4   Tm      578 non-null    object 
 5   G       578 non-null    int64  
 6   GS      578 non-null    int64  
 7   MP      578 non-null    int64  
 8   FG      578 non-null    int64  
 9   FGA     578 non-null    int64  
 10  FG%     575 non-null    float64
 11  3P      578 non-null    int64  
 12  3PA     578 non-null    int64  
 13  3P%     522 non-null    float64
 14  2P      578 non-null    int64  
 15  2PA     578 non-null    int64  
 16  2P%     570 non-null    float64
 17  eFG%    575 non-null    float64
 18  FT      578 non-null    int64  
 19  FTA     578 non-null    int64  
 20  FT%     554 non-null    float64
 21  ORB     578 non-null    int64  
 22  DRB     578 non-null    int64  
 23  TRB     578 non-null    int64  
 24  AST     578 non-null    int64  
 25  STL     578 non-null    int64  
 26  BLK     578 non-null    int64  
 27  TOV     578 non-null    int64  
 28  PF      578 non-null    int64  
 29  PTS     578 non-null    int64  
dtypes: float64(5), int64(22), object(3)
memory usage: 135.6+ KB

	Rk	Player	Pos	Age	Tm	G	GS	MP	FG	FGA	...	FT%	ORB	DRB	TRB	AST	STL	BLK	TOV	PF	PTS
0	1	Quincy Acy	PF	25	SAC	59	29	876	119	214	...	0.735	65	123	188	27	29	24	27	103	307
1	2	Jordan Adams	SG	21	MEM	2	0	15	2	6	...	0.600	0	2	2	3	3	0	2	2	7
2	3	Steven Adams	C	22	OKC	80	80	2014	261	426	...	0.582	219	314	533	62	42	89	84	223	636
3	4	Arron Afflalo	SG	30	NYK	71	57	2371	354	799	...	0.840	23	243	266	144	25	10	82	142	909
4	5	Alexis Ajinça	C	27	NOP	59	17	861	150	315	...	0.839	75	194	269	31	19	36	54	134	352

5 rows × 30 columns

	Rk	Player	Pos	Age	Tm	G	GS	MP	FG	FGA	...	FT%	ORB	DRB	TRB	AST	STL	BLK	TOV	PF	PTS
573	472	Joe Young	PG	23	IND	41	0	384	62	169	...	0.800	6	44	50	65	15	0	33	30	154
574	473	Nick Young	SG	30	LAL	54	2	1033	126	372	...	0.829	14	83	97	34	23	7	30	50	392
575	474	Thaddeus Young	PF	27	BRK	73	73	2407	495	963	...	0.644	176	484	660	136	112	37	136	182	1102
576	475	Cody Zeller	C	23	CHO	73	60	1774	231	437	...	0.754	138	317	455	71	57	63	68	204	638
577	476	Tyler Zeller	C	26	BOS	60	3	710	138	290	...	0.815	62	116	178	29	10	22	46	97	364

5 rows × 30 columns

import pandas as pd

from IPython.core.interactiveshell import InteractiveShell
InteractiveShell.ast_node_interactivity = "all"

nba_stats_season2 = pd.read_csv('nba_stats_2016_2017.csv')

nba_stats_season2.info()
nba_stats_season2.head()
nba_stats_season2.tail()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 595 entries, 0 to 594
Data columns (total 30 columns):
 #   Column  Non-Null Count  Dtype  
---  ------  --------------  -----  
 0   Rk      595 non-null    int64  
 1   Player  595 non-null    object 
 2   Pos     595 non-null    object 
 3   Age     595 non-null    int64  
 4   Tm      595 non-null    object 
 5   G       595 non-null    int64  
 6   GS      595 non-null    int64  
 7   MP      595 non-null    int64  
 8   FG      595 non-null    int64  
 9   FGA     595 non-null    int64  
 10  FG%     593 non-null    float64
 11  3P      595 non-null    int64  
 12  3PA     595 non-null    int64  
 13  3P%     549 non-null    float64
 14  2P      595 non-null    int64  
 15  2PA     595 non-null    int64  
 16  2P%     590 non-null    float64
 17  eFG%    593 non-null    float64
 18  FT      595 non-null    int64  
 19  FTA     595 non-null    int64  
 20  FT%     571 non-null    float64
 21  ORB     595 non-null    int64  
 22  DRB     595 non-null    int64  
 23  TRB     595 non-null    int64  
 24  AST     595 non-null    int64  
 25  STL     595 non-null    int64  
 26  BLK     595 non-null    int64  
 27  TOV     595 non-null    int64  
 28  PF      595 non-null    int64  
 29  PTS     595 non-null    int64  
dtypes: float64(5), int64(22), object(3)
memory usage: 139.6+ KB

	Rk	Player	Pos	Age	Tm	G	GS	MP	FG	FGA	...	FT%	ORB	DRB	TRB	AST	STL	BLK	TOV	PF	PTS
0	1	Álex Abrines	SG	23	OKC	68	6	1055	134	341	...	0.898	18	68	86	40	37	8	33	114	406
1	2	Quincy Acy	PF	26	TOT	38	1	558	70	170	...	0.750	20	95	115	18	14	15	21	67	222
2	2	Quincy Acy	PF	26	DAL	6	0	48	5	17	...	0.667	2	6	8	0	0	0	2	9	13
3	2	Quincy Acy	PF	26	BRK	32	1	510	65	153	...	0.754	18	89	107	18	14	15	19	58	209
4	3	Steven Adams	C	23	OKC	80	80	2389	374	655	...	0.611	281	332	613	86	89	78	146	195	905

5 rows × 30 columns

	Rk	Player	Pos	Age	Tm	G	GS	MP	FG	FGA	...	FT%	ORB	DRB	TRB	AST	STL	BLK	TOV	PF	PTS
590	482	Cody Zeller	C	24	CHO	62	58	1725	253	443	...	0.679	135	270	405	99	62	58	65	189	639
591	483	Tyler Zeller	C	27	BOS	51	5	525	78	158	...	0.564	43	81	124	42	7	21	20	61	178
592	484	Stephen Zimmerman	C	20	ORL	19	0	108	10	31	...	0.600	11	24	35	4	2	5	3	17	23
593	485	Paul Zipser	SF	22	CHI	44	18	843	88	221	...	0.775	15	110	125	36	15	16	40	78	240
594	486	Ivica Zubac	C	19	LAL	38	11	609	126	238	...	0.653	41	118	159	30	14	33	30	66	284

5 rows × 30 columns

import pandas as pd

from IPython.core.interactiveshell import InteractiveShell
InteractiveShell.ast_node_interactivity = "all"

nba_stats_season3 = pd.read_csv('nba_stats_2017_2018.csv')

nba_stats_season3.info()
nba_stats_season3.head()
nba_stats_season3.tail()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 664 entries, 0 to 663
Data columns (total 30 columns):
 #   Column  Non-Null Count  Dtype  
---  ------  --------------  -----  
 0   Rk      664 non-null    int64  
 1   Player  664 non-null    object 
 2   Pos     664 non-null    object 
 3   Age     664 non-null    int64  
 4   Tm      664 non-null    object 
 5   G       664 non-null    int64  
 6   GS      664 non-null    int64  
 7   MP      664 non-null    int64  
 8   FG      664 non-null    int64  
 9   FGA     664 non-null    int64  
 10  FG%     660 non-null    float64
 11  3P      664 non-null    int64  
 12  3PA     664 non-null    int64  
 13  3P%     599 non-null    float64
 14  2P      664 non-null    int64  
 15  2PA     664 non-null    int64  
 16  2P%     646 non-null    float64
 17  eFG%    660 non-null    float64
 18  FT      664 non-null    int64  
 19  FTA     664 non-null    int64  
 20  FT%     606 non-null    float64
 21  ORB     664 non-null    int64  
 22  DRB     664 non-null    int64  
 23  TRB     664 non-null    int64  
 24  AST     664 non-null    int64  
 25  STL     664 non-null    int64  
 26  BLK     664 non-null    int64  
 27  TOV     664 non-null    int64  
 28  PF      664 non-null    int64  
 29  PTS     664 non-null    int64  
dtypes: float64(5), int64(22), object(3)
memory usage: 155.8+ KB

	Rk	Player	Pos	Age	Tm	G	GS	MP	FG	FGA	...	FT%	ORB	DRB	TRB	AST	STL	BLK	TOV	PF	PTS
0	1	Álex Abrines	SG	24	OKC	75	8	1134	115	291	...	0.848	26	88	114	28	38	8	25	124	353
1	2	Quincy Acy	PF	27	BRK	70	8	1359	130	365	...	0.817	40	217	257	57	33	29	60	149	411
2	3	Steven Adams	C	24	OKC	76	76	2487	448	712	...	0.559	384	301	685	88	92	78	128	215	1056
3	4	Bam Adebayo	C	20	MIA	69	19	1368	174	340	...	0.721	118	263	381	101	32	41	66	138	477
4	5	Arron Afflalo	SG	32	ORL	53	3	682	65	162	...	0.846	4	62	66	30	4	9	21	56	179

5 rows × 30 columns

	Rk	Player	Pos	Age	Tm	G	GS	MP	FG	FGA	...	FT%	ORB	DRB	TRB	AST	STL	BLK	TOV	PF	PTS
659	537	Tyler Zeller	C	28	BRK	42	33	703	125	229	...	0.667	63	131	194	28	8	21	35	78	300
660	537	Tyler Zeller	C	28	MIL	24	1	406	62	105	...	0.895	47	64	111	19	7	14	12	48	141
661	538	Paul Zipser	SF	23	CHI	54	12	824	81	234	...	0.760	13	118	131	46	20	15	43	86	218
662	539	Ante Žižić	C	21	CLE	32	2	214	49	67	...	0.724	24	36	60	5	2	13	11	30	119
663	540	Ivica Zubac	C	20	LAL	43	0	410	61	122	...	0.765	45	78	123	25	8	15	26	47	161

5 rows × 30 columns

import pandas as pd

from IPython.core.interactiveshell import InteractiveShell
InteractiveShell.ast_node_interactivity = "all"

nba_stats_season4 = pd.read_csv('nba_stats_2018_2019.csv')

nba_stats_season4.info()
nba_stats_season4.head()
nba_stats_season4.tail()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 708 entries, 0 to 707
Data columns (total 30 columns):
 #   Column  Non-Null Count  Dtype  
---  ------  --------------  -----  
 0   Rk      708 non-null    int64  
 1   Player  708 non-null    object 
 2   Pos     708 non-null    object 
 3   Age     708 non-null    int64  
 4   Tm      708 non-null    object 
 5   G       708 non-null    int64  
 6   GS      708 non-null    int64  
 7   MP      708 non-null    int64  
 8   FG      708 non-null    int64  
 9   FGA     708 non-null    int64  
 10  FG%     702 non-null    float64
 11  3P      708 non-null    int64  
 12  3PA     708 non-null    int64  
 13  3P%     661 non-null    float64
 14  2P      708 non-null    int64  
 15  2PA     708 non-null    int64  
 16  2P%     693 non-null    float64
 17  eFG%    702 non-null    float64
 18  FT      708 non-null    int64  
 19  FTA     708 non-null    int64  
 20  FT%     665 non-null    float64
 21  ORB     708 non-null    int64  
 22  DRB     708 non-null    int64  
 23  TRB     708 non-null    int64  
 24  AST     708 non-null    int64  
 25  STL     708 non-null    int64  
 26  BLK     708 non-null    int64  
 27  TOV     708 non-null    int64  
 28  PF      708 non-null    int64  
 29  PTS     708 non-null    int64  
dtypes: float64(5), int64(22), object(3)
memory usage: 166.1+ KB

	Rk	Player	Pos	Age	Tm	G	GS	MP	FG	FGA	...	FT%	ORB	DRB	TRB	AST	STL	BLK	TOV	PF	PTS
0	1	Álex Abrines	SG	25	OKC	31	2	588	56	157	...	0.923	5	43	48	20	17	6	14	53	165
1	2	Quincy Acy	PF	28	PHO	10	0	123	4	18	...	0.700	3	22	25	8	1	4	4	24	17
2	3	Jaylen Adams	PG	22	ATL	34	1	428	38	110	...	0.778	11	49	60	65	14	5	28	45	108
3	4	Steven Adams	C	25	OKC	80	80	2669	481	809	...	0.500	391	369	760	124	117	76	135	204	1108
4	5	Bam Adebayo	C	21	MIA	82	28	1913	280	486	...	0.735	165	432	597	184	71	65	121	203	729

5 rows × 30 columns

	Rk	Player	Pos	Age	Tm	G	GS	MP	FG	FGA	...	FT%	ORB	DRB	TRB	AST	STL	BLK	TOV	PF	PTS
703	528	Tyler Zeller	C	29	MEM	4	1	82	16	28	...	0.778	9	9	18	3	1	3	4	16	46
704	529	Ante Žižić	C	22	CLE	59	25	1082	183	331	...	0.705	108	212	320	53	13	22	61	113	459
705	530	Ivica Zubac	C	21	TOT	59	37	1040	212	379	...	0.802	115	247	362	63	14	51	70	137	525
706	530	Ivica Zubac	C	21	LAL	33	12	516	112	193	...	0.864	54	108	162	25	4	27	33	73	281
707	530	Ivica Zubac	C	21	LAC	26	25	524	100	186	...	0.733	61	139	200	38	10	24	37	64	244

5 rows × 30 columns

import pandas as pd

from IPython.core.interactiveshell import InteractiveShell
InteractiveShell.ast_node_interactivity = "all"

nba_stats_season5 = pd.read_csv('nba_stats_2019_2020.csv')

nba_stats_season5.info()
nba_stats_season5.head()
nba_stats_season5.tail()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 651 entries, 0 to 650
Data columns (total 30 columns):
 #   Column  Non-Null Count  Dtype  
---  ------  --------------  -----  
 0   Rk      651 non-null    int64  
 1   Player  651 non-null    object 
 2   Pos     651 non-null    object 
 3   Age     651 non-null    int64  
 4   Tm      651 non-null    object 
 5   G       651 non-null    int64  
 6   GS      651 non-null    int64  
 7   MP      651 non-null    int64  
 8   FG      651 non-null    int64  
 9   FGA     651 non-null    int64  
 10  FG%     649 non-null    float64
 11  3P      651 non-null    int64  
 12  3PA     651 non-null    int64  
 13  3P%     616 non-null    float64
 14  2P      651 non-null    int64  
 15  2PA     651 non-null    int64  
 16  2P%     645 non-null    float64
 17  eFG%    649 non-null    float64
 18  FT      651 non-null    int64  
 19  FTA     651 non-null    int64  
 20  FT%     618 non-null    float64
 21  ORB     651 non-null    int64  
 22  DRB     651 non-null    int64  
 23  TRB     651 non-null    int64  
 24  AST     651 non-null    int64  
 25  STL     651 non-null    int64  
 26  BLK     651 non-null    int64  
 27  TOV     651 non-null    int64  
 28  PF      651 non-null    int64  
 29  PTS     651 non-null    int64  
dtypes: float64(5), int64(22), object(3)
memory usage: 152.7+ KB

	Rk	Player	Pos	Age	Tm	G	GS	MP	FG	FGA	...	FT%	ORB	DRB	TRB	AST	STL	BLK	TOV	PF	PTS
0	1	Steven Adams	C	26	OKC	63	63	1680	283	478	...	0.582	207	376	583	146	51	67	94	122	684
1	2	Bam Adebayo	PF	22	MIA	72	72	2417	440	790	...	0.691	176	559	735	368	82	93	204	182	1146
2	3	LaMarcus Aldridge	C	34	SAS	53	53	1754	391	793	...	0.827	103	289	392	129	36	87	74	128	1001
3	4	Kyle Alexander	C	23	MIA	2	0	13	1	2	...	NaN	2	1	3	0	0	0	1	1	2
4	5	Nickeil Alexander-Walker	SG	21	NOP	47	1	591	98	266	...	0.676	9	75	84	89	17	8	54	57	267

5 rows × 30 columns

	Rk	Player	Pos	Age	Tm	G	GS	MP	FG	FGA	...	FT%	ORB	DRB	TRB	AST	STL	BLK	TOV	PF	PTS
646	525	Trae Young	PG	21	ATL	60	60	2120	546	1249	...	0.860	32	223	255	560	65	8	289	104	1778
647	526	Cody Zeller	C	27	CHO	58	39	1341	251	479	...	0.682	160	251	411	88	40	25	75	140	642
648	527	Tyler Zeller	C	30	SAS	2	0	4	1	4	...	NaN	3	1	4	0	0	0	0	0	2
649	528	Ante Žižić	C	23	CLE	22	0	221	41	72	...	0.737	18	48	66	6	7	5	10	27	96
650	529	Ivica Zubac	C	22	LAC	72	70	1326	236	385	...	0.747	197	346	543	82	16	66	61	168	596

5 rows × 30 columns

import pandas as pd

from IPython.core.interactiveshell import InteractiveShell
InteractiveShell.ast_node_interactivity = "all"

nba_playoffs_data = pd.read_csv('nba_playoffs_data.csv')

nba_playoffs_data.info()
nba_playoffs_data.head()
nba_playoffs_data.tail()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 80 entries, 0 to 79
Data columns (total 3 columns):
 #   Column   Non-Null Count  Dtype 
---  ------   --------------  ----- 
 0   Year     80 non-null     object
 1   Tm       80 non-null     object
 2   Playoff  80 non-null     object
dtypes: object(3)
memory usage: 2.0+ KB

	Year	Tm	Playoff
0	2019-2020	MIL	Y
1	2019-2020	ORL	Y
2	2019-2020	IND	Y
3	2019-2020	MIA	Y
4	2019-2020	BOS	Y

	Year	Tm	Playoff
75	2015-2016	POR	Y
76	2015-2016	OKC	Y
77	2015-2016	DAL	Y
78	2015-2016	SAS	Y
79	2015-2016	MEM	Y

STOP HERE for your Project Proposal assignment. Submit your (1) original data file(s) along with (2) the completed notebook up to this point, and (3) the html file for grading and approval.

Instructor Feedback and Approval (3 pts): Your instructor will provide feedback in either the cell below this or via Canvas. You can expect one of the following point values for this portion. 3 pts - if your project goals and data set are both approved.
2 pts - if your data set is approved but changes to your project goals (Step 3) are needed.
1 pt - if your project goals are approved but your data set is not approved.
0 pts - if neither your data set nor your project goals are approved.

As needed, follow your instructor's feeback and guidance to get on track for the remaining portions of the EDA project.

EDA Phase 1

The overall goal of this assignment is to take all necessary steps to inspect the quality of your data and prepare the data according to your needs. For information and resources on the process of Exploratory Data Analysis (EDA), you should explore the EDA Project Resources Module in Canvas. Once you’ve read through the information provided in that module and have a comfortable understanding of EDA using Python, complete steps 6 through 10 listed below to satisfy the requirements for your EDA Phase 1 assignment. **Remember to convert code cells provided to markdown cells for any typed responses to questions.**

Step 6 (2 pts): Begin by elaborating in more detail from the previous assignment on why you chose this data?
1. Explain what you hope to learn from this data. 2. Do you have a hunch about what this data will reveal? (The answer to this question will be used in the Introduction section of your EDA report.)

I hope to learn and find positive or negative correlation between how NBA players average scoring points in the 3 pointers and 2 pointers shooting category can affect their Teams chances of making the NBA playoffs. Also, explore and identify relationship between NBA player positions that can be used to analyze the data in the shooting category.

I have a hunch this data will reveal that NBA players shooting an average of higher 3 pointers per game have good offensive stats in categories like '3Pointers', 'FieldsPerGame', and 'TotalPointsPerGame' are typically more likely to score more to lead in 'TotalPoints' and be in 'Team' that makes 'Playoff' in a season than players who score an average of 2 pointers per game. Also, based on the NBA player's position for Shooting Guard (SG) and Point Guard (PG) are more likely to score more 3 pointers per games and leads in total points per season compare to position such Center(C), Small-Forwards (SF), and Power-Forwards (PF) are likely to score more 2 pointers per game.

Step 7 (2 pts): Discuss the popluation and the sample:
1. What is the population being represented by the data you’ve chosen? 2. What is the total sample size?

The population being represented is overall NBA Player statistics 2015-2020 also reference data to check which players are in the Teams that made the playoffs during the following seasons.

The sample size is 3196 rows and 37 columns which contains NBA players statistics and playoffs reference data from 2015-2020

Step 8 (2 pts): Describe how the data was collected. For example, is this a random sample? Are sampling weights used with the data?

The data was collected from the following website https://www.basketball-reference.com/ which is publicly available for variety of NBA stats, and our data focuses on overall NBA Players Statistics from the year 2015-2020 seasons which is downloaded from this site https://www.basketball-reference.com/leagues/NBA_2020_totals.html

This is not random sample and sampling weights are not used with the data

Step 9 (4 pts): In the Project Proposal assignment you used the info( ) method to inspect the variables, their data types, and the number of non-null values. Using that information as a guide, provide definitions of each of your variables and their corresponding data types, i.e. a data dictionary. Also indicate which variables will be used for your purposes.

	Variables	Definition	DataType	Will be Used
1	Rk	Rank of the players for each season based on overall statistics	Integer
2	Player	Player corresponds to name of the NBA players for all each season	String	X
3	Pos	Pos is the position of the NBA players while playing the game	String	X
4	Age	Age corresponds to age of the NBA players	String	X
5	Tm	Tm corresponds to team of the NBA players	String	X
6	G	G corresponds to the number of games played by NBA player in that season	Integer	X
7	GS	GS corresponds to the number of games started by NBA players in that season	Integer
8	MP	MP corresponds to the number of minutes played by NBA players in that season	Integer	X
9	FG	The number of field goals that a NBA players have made. This includes both 2 pointers and 3 pointers	Integer	X
10	FGA	The number of field goals that a NBA players have attempted. This includes both 2 pointers and 3 pointers	Integer	X
11	FG%	The percentage of field goal attempts that a NBA player makes in that season	Float	X
12	3P	The number of 3 pointers field goals that a NBA players have made in that season	Integer	X
13	3PA	The number of 3 pointers field goals that a NBA players have attempted in that season	Integer	X
14	3P%	The percentage of 3 pointers field goal attempts that a NBA player makes in that season	Float	X
15	2P	The number of 2 pointers field goals that a NBA players have made in that season	Integer	X
16	2PA	The number of 2 pointers field goals that a NBA players have attempted in that season	Integer	X
17	2P%	The percentage of 2 pointers field goal attempts that a NBA player makes in that season	Float	X
18	eFG%	It is effective field goal percentage that adjusts for the fact that a 3-pointer field goal is worth one more point than a 2-pointer field goal	Float	X
19	FT	The number of free throws that a NBA players have made in that season	Integer
20	FTA	The number of free throws that a NBA players have attempted in that season	Integer
21	FT%	The percentage of free throw attempts that a NBA player makes in that season	Float
22	ORB	The number of offensive rebounds an NBA player has collected while they were playing on offense in that season	Integer
23	DRB	The number of defensive rebounds an NBA player has collected while they were playing on defense in that season	Integer
24	TRB	The number of total rebounds an NBA player has collected while they were playing in that season	Integer
25	AST	The number of assists is a pass made to another player that lead directly to a basket point	Integer
26	STL	The number of times an NBA defensive player takes the ball from a player on offense, while playing game in that season	Integer
27	BLK	A block occurs when offensive NBA player attempts a shot, and the defense player tips the ball, blocking their chance to score a point	Integer
28	TOV	A turnover occurs when the NBA player on offense loses the ball to the defense data is collected for each NBA player in that season	Integer
29	PF	The number of personal fouls an NBA player has committed in that season	Integer
30	PTS	The number of points scored by an NBA player in that season	Integer	X
31	Year	The Year is reference data column to keep track of NBA players statistics from each season	String	X
32	Playoff	The Playoff is reference data column to keep track of which NBA Players were in the team made playoffs in corresponding seasons	String	X
33	MinutesPerGame	The MinutesPerGame is calculated reference based on the 'MinutesPlayed' divided by 'GamesPlayed' column because earlier these columns have stats per seasons. To have better analysis and interpretation of the data.	Float	X
34	FieldGoalsPerGame	The FieldGoalsPerGame is calculated reference based on the 'FieldGoals' divided by 'GamesPlayed' column because earlier these columns have stats per seasons. To have better analysis and interpretation of the data we have created Field Goals per game.	Float	X
35	3PointerPerGame	The 3PointerPerGame is calculated reference based on the '3Pointers' divided by 'GamesPlayed' column because earlier these columns have stats per seasons. To have better analysis and interpretation of the data.	Float	X
36	2PointerPerGame	The 2PointerPerGame is calculated reference based on the '2Pointers' divided by 'GamesPlayed' column because earlier these columns have stats per seasons. To have better analysis and interpretation of the data.	Float	X
37	TotalPointsPerGame	The TotalPointsPerGame is calculated reference based on the 'TotalPoints' divided by 'GamesPlayed' column because earlier these columns have stats per seasons. To have better analysis and interpretation of the data.	Float	X

Step 10 (10 pts): For full credit in this problem you'll want to take all necessary steps to report on the quality of the data and clean the data accordingly. Some things to consider while doing this are listed below. Depending on your data and goals, there may be additional steps needed than those listed here. 1. Are there rows with missing or inconsistent values? If so, eliminate those rows from your data where appropriate. 2. Are there any outliers or duplicate rows? If so, eliminate those rows from your data where appropriate. At each stage of cleaning the data, state how many rows were eliminated. 3. Are you using all columns (variables) in the data? If not, are you eliminating those columns? 4. Consider some type of visual display such as a boxplot to determine any outliers. Do any outliers need removed? If so, how many were removed? At each stage of cleaning the data, state how many rows were eliminated. It is good practice to get the shape of the data before and after each step in cleaning the data and add typed explanations (in separate markdown cells) of the steps taken to clean the data.

Include the rest of your work below and insert cells where needed.

The first step is adding year column to all the corresponding NBA statistics seasons dataframe. Second step is to concatenate all the NBA seasons dataframe into one large dataset assigning to NBA Players Statistics dataframe. Third step is merging nba_player_stats dataframe we made in second step above with nba_playoffs_data dataframe doing outer join on Year and Team column, so that after merge we can analyze which Teams made the playoffs in what Year, assign the new joined table to overall_nba_playoffs_stats data.

nba_stats_season1['Year'] = "2015-2016" #Adding Year column to this dataframe since data represents 2015-2016 stats
nba_stats_season1.head(5)

nba_stats_season2['Year'] = "2016-2017" #Adding Year column to this dataframe since data represents 2016-2017 stats
nba_stats_season2.head(5)

nba_stats_season3['Year'] = "2017-2018" #Adding Year column to this dataframe since data represents 2017-2018 stats
nba_stats_season3.head(5)

nba_stats_season4['Year'] = "2018-2019" #Adding Year column to this dataframe since data represents 2018-2019 stats
nba_stats_season4.head(5)

nba_stats_season5['Year'] = "2019-2020" #Adding Year column to this dataframe since data represents 2019-2020 stats
nba_stats_season5.head(5)

	Rk	Player	Pos	Age	Tm	G	GS	MP	FG	FGA	...	ORB	DRB	TRB	AST	STL	BLK	TOV	PF	PTS	Year
0	1	Quincy Acy	PF	25	SAC	59	29	876	119	214	...	65	123	188	27	29	24	27	103	307	2015-2016
1	2	Jordan Adams	SG	21	MEM	2	0	15	2	6	...	0	2	2	3	3	0	2	2	7	2015-2016
2	3	Steven Adams	C	22	OKC	80	80	2014	261	426	...	219	314	533	62	42	89	84	223	636	2015-2016
3	4	Arron Afflalo	SG	30	NYK	71	57	2371	354	799	...	23	243	266	144	25	10	82	142	909	2015-2016
4	5	Alexis Ajinça	C	27	NOP	59	17	861	150	315	...	75	194	269	31	19	36	54	134	352	2015-2016

5 rows × 31 columns

	Rk	Player	Pos	Age	Tm	G	GS	MP	FG	FGA	...	ORB	DRB	TRB	AST	STL	BLK	TOV	PF	PTS	Year
0	1	Álex Abrines	SG	23	OKC	68	6	1055	134	341	...	18	68	86	40	37	8	33	114	406	2016-2017
1	2	Quincy Acy	PF	26	TOT	38	1	558	70	170	...	20	95	115	18	14	15	21	67	222	2016-2017
2	2	Quincy Acy	PF	26	DAL	6	0	48	5	17	...	2	6	8	0	0	0	2	9	13	2016-2017
3	2	Quincy Acy	PF	26	BRK	32	1	510	65	153	...	18	89	107	18	14	15	19	58	209	2016-2017
4	3	Steven Adams	C	23	OKC	80	80	2389	374	655	...	281	332	613	86	89	78	146	195	905	2016-2017

5 rows × 31 columns

	Rk	Player	Pos	Age	Tm	G	GS	MP	FG	FGA	...	ORB	DRB	TRB	AST	STL	BLK	TOV	PF	PTS	Year
0	1	Álex Abrines	SG	24	OKC	75	8	1134	115	291	...	26	88	114	28	38	8	25	124	353	2017-2018
1	2	Quincy Acy	PF	27	BRK	70	8	1359	130	365	...	40	217	257	57	33	29	60	149	411	2017-2018
2	3	Steven Adams	C	24	OKC	76	76	2487	448	712	...	384	301	685	88	92	78	128	215	1056	2017-2018
3	4	Bam Adebayo	C	20	MIA	69	19	1368	174	340	...	118	263	381	101	32	41	66	138	477	2017-2018
4	5	Arron Afflalo	SG	32	ORL	53	3	682	65	162	...	4	62	66	30	4	9	21	56	179	2017-2018

5 rows × 31 columns

	Rk	Player	Pos	Age	Tm	G	GS	MP	FG	FGA	...	ORB	DRB	TRB	AST	STL	BLK	TOV	PF	PTS	Year
0	1	Álex Abrines	SG	25	OKC	31	2	588	56	157	...	5	43	48	20	17	6	14	53	165	2018-2019
1	2	Quincy Acy	PF	28	PHO	10	0	123	4	18	...	3	22	25	8	1	4	4	24	17	2018-2019
2	3	Jaylen Adams	PG	22	ATL	34	1	428	38	110	...	11	49	60	65	14	5	28	45	108	2018-2019
3	4	Steven Adams	C	25	OKC	80	80	2669	481	809	...	391	369	760	124	117	76	135	204	1108	2018-2019
4	5	Bam Adebayo	C	21	MIA	82	28	1913	280	486	...	165	432	597	184	71	65	121	203	729	2018-2019

5 rows × 31 columns

	Rk	Player	Pos	Age	Tm	G	GS	MP	FG	FGA	...	ORB	DRB	TRB	AST	STL	BLK	TOV	PF	PTS	Year
0	1	Steven Adams	C	26	OKC	63	63	1680	283	478	...	207	376	583	146	51	67	94	122	684	2019-2020
1	2	Bam Adebayo	PF	22	MIA	72	72	2417	440	790	...	176	559	735	368	82	93	204	182	1146	2019-2020
2	3	LaMarcus Aldridge	C	34	SAS	53	53	1754	391	793	...	103	289	392	129	36	87	74	128	1001	2019-2020
3	4	Kyle Alexander	C	23	MIA	2	0	13	1	2	...	2	1	3	0	0	0	1	1	2	2019-2020
4	5	Nickeil Alexander-Walker	SG	21	NOP	47	1	591	98	266	...	9	75	84	89	17	8	54	57	267	2019-2020

5 rows × 31 columns

We are going concatenate all the NBA seaons dataframe where we added Year column into one large dataset assigning to NBA Players Statistics dataframe represents the following variable nba_players_stats mentioned below.

nba_players_stats = pd.concat([nba_stats_season1, nba_stats_season2, nba_stats_season3, nba_stats_season4, nba_stats_season5])
nba_players_stats.shape
nba_players_stats.info()
nba_players_stats.head()

(3196, 31)

<class 'pandas.core.frame.DataFrame'>
Int64Index: 3196 entries, 0 to 650
Data columns (total 31 columns):
 #   Column  Non-Null Count  Dtype  
---  ------  --------------  -----  
 0   Rk      3196 non-null   int64  
 1   Player  3196 non-null   object 
 2   Pos     3196 non-null   object 
 3   Age     3196 non-null   int64  
 4   Tm      3196 non-null   object 
 5   G       3196 non-null   int64  
 6   GS      3196 non-null   int64  
 7   MP      3196 non-null   int64  
 8   FG      3196 non-null   int64  
 9   FGA     3196 non-null   int64  
 10  FG%     3179 non-null   float64
 11  3P      3196 non-null   int64  
 12  3PA     3196 non-null   int64  
 13  3P%     2947 non-null   float64
 14  2P      3196 non-null   int64  
 15  2PA     3196 non-null   int64  
 16  2P%     3144 non-null   float64
 17  eFG%    3179 non-null   float64
 18  FT      3196 non-null   int64  
 19  FTA     3196 non-null   int64  
 20  FT%     3014 non-null   float64
 21  ORB     3196 non-null   int64  
 22  DRB     3196 non-null   int64  
 23  TRB     3196 non-null   int64  
 24  AST     3196 non-null   int64  
 25  STL     3196 non-null   int64  
 26  BLK     3196 non-null   int64  
 27  TOV     3196 non-null   int64  
 28  PF      3196 non-null   int64  
 29  PTS     3196 non-null   int64  
 30  Year    3196 non-null   object 
dtypes: float64(5), int64(22), object(4)
memory usage: 799.0+ KB

	Rk	Player	Pos	Age	Tm	G	GS	MP	FG	FGA	...	ORB	DRB	TRB	AST	STL	BLK	TOV	PF	PTS	Year
0	1	Quincy Acy	PF	25	SAC	59	29	876	119	214	...	65	123	188	27	29	24	27	103	307	2015-2016
1	2	Jordan Adams	SG	21	MEM	2	0	15	2	6	...	0	2	2	3	3	0	2	2	7	2015-2016
2	3	Steven Adams	C	22	OKC	80	80	2014	261	426	...	219	314	533	62	42	89	84	223	636	2015-2016
3	4	Arron Afflalo	SG	30	NYK	71	57	2371	354	799	...	23	243	266	144	25	10	82	142	909	2015-2016
4	5	Alexis Ajinça	C	27	NOP	59	17	861	150	315	...	75	194	269	31	19	36	54	134	352	2015-2016

5 rows × 31 columns

Step 1: We are merging nba_player_stats dataframe which is the concatenated dataframe with nba_playoffs_data reference dataframe which includes Year, Team, and Playoffs column shows 'Y' because all the Teams in the playoffs reference made the playoffs

Step 2: We did an outer join on Year and Team column, so that returns all the rows from the left dataframe, all the rows from the right dataframe, and matches up based on the Year, Team and Playoffs that represents 'Y' who made the playoffs. Also, with NaNs elsewhere for the Teams that did not make the playoffs

overall_nba_playoffs_stats = pd.merge(nba_players_stats, nba_playoffs_data, how="outer", on=["Year", "Tm"])
overall_nba_playoffs_stats.shape
overall_nba_playoffs_stats.info()
overall_nba_playoffs_stats

(3196, 32)

<class 'pandas.core.frame.DataFrame'>
Int64Index: 3196 entries, 0 to 3195
Data columns (total 32 columns):
 #   Column   Non-Null Count  Dtype  
---  ------   --------------  -----  
 0   Rk       3196 non-null   int64  
 1   Player   3196 non-null   object 
 2   Pos      3196 non-null   object 
 3   Age      3196 non-null   int64  
 4   Tm       3196 non-null   object 
 5   G        3196 non-null   int64  
 6   GS       3196 non-null   int64  
 7   MP       3196 non-null   int64  
 8   FG       3196 non-null   int64  
 9   FGA      3196 non-null   int64  
 10  FG%      3179 non-null   float64
 11  3P       3196 non-null   int64  
 12  3PA      3196 non-null   int64  
 13  3P%      2947 non-null   float64
 14  2P       3196 non-null   int64  
 15  2PA      3196 non-null   int64  
 16  2P%      3144 non-null   float64
 17  eFG%     3179 non-null   float64
 18  FT       3196 non-null   int64  
 19  FTA      3196 non-null   int64  
 20  FT%      3014 non-null   float64
 21  ORB      3196 non-null   int64  
 22  DRB      3196 non-null   int64  
 23  TRB      3196 non-null   int64  
 24  AST      3196 non-null   int64  
 25  STL      3196 non-null   int64  
 26  BLK      3196 non-null   int64  
 27  TOV      3196 non-null   int64  
 28  PF       3196 non-null   int64  
 29  PTS      3196 non-null   int64  
 30  Year     3196 non-null   object 
 31  Playoff  1491 non-null   object 
dtypes: float64(5), int64(22), object(5)
memory usage: 824.0+ KB

	Rk	Player	Pos	Age	Tm	G	GS	MP	FG	FGA	...	DRB	TRB	AST	STL	BLK	TOV	PF	PTS	Year	Playoff
0	1	Quincy Acy	PF	25	SAC	59	29	876	119	214	...	123	188	27	29	24	27	103	307	2015-2016	NaN
1	15	James Anderson	SG	26	SAC	51	15	721	67	178	...	73	86	41	21	14	42	54	179	2015-2016	NaN
2	44	Marco Belinelli	SG	29	SAC	68	7	1672	245	635	...	107	117	127	37	2	80	91	696	2015-2016	NaN
3	71	Caron Butler	SF	35	SAC	17	1	176	25	59	...	17	22	10	9	1	3	19	63	2015-2016	NaN
4	82	Omri Casspi	PF	27	SAC	69	21	1880	299	622	...	352	410	95	56	17	94	154	813	2015-2016	NaN
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
3191	495	Kemba Walker	PG	29	BOS	56	56	1742	378	889	...	181	217	268	48	28	117	91	1145	2019-2020	Y
3192	499	Brad Wanamaker	PG	30	BOS	71	1	1369	162	362	...	122	144	179	61	14	76	133	487	2019-2020	Y
3193	503	Tremont Waters	PG	22	BOS	11	1	119	14	49	...	12	12	16	10	2	15	13	40	2019-2020	Y
3194	511	Grant Williams	PF	21	BOS	69	5	1043	87	211	...	119	178	68	30	36	50	163	237	2019-2020	Y
3195	516	Robert Williams	C	22	BOS	29	1	388	64	88	...	88	128	27	22	35	21	51	150	2019-2020	Y

3196 rows × 32 columns

We are checking for the rows in the dataframe below to see which Teams did not make the playoffs, these are NaNs values after the outer join on the dataframe we are making these corresponding NaNs values to Playoff = 'N' which means that these NBA players and Teams did not make the playoffs in that Year based on the historical five year data

overall_nba_playoffs_stats.loc[overall_nba_playoffs_stats.Playoff != "Y", "Playoff"] = "N"
overall_nba_playoffs_stats

	Rk	Player	Pos	Age	Tm	G	GS	MP	FG	FGA	...	DRB	TRB	AST	STL	BLK	TOV	PF	PTS	Year	Playoff
0	1	Quincy Acy	PF	25	SAC	59	29	876	119	214	...	123	188	27	29	24	27	103	307	2015-2016	N
1	15	James Anderson	SG	26	SAC	51	15	721	67	178	...	73	86	41	21	14	42	54	179	2015-2016	N
2	44	Marco Belinelli	SG	29	SAC	68	7	1672	245	635	...	107	117	127	37	2	80	91	696	2015-2016	N
3	71	Caron Butler	SF	35	SAC	17	1	176	25	59	...	17	22	10	9	1	3	19	63	2015-2016	N
4	82	Omri Casspi	PF	27	SAC	69	21	1880	299	622	...	352	410	95	56	17	94	154	813	2015-2016	N
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
3191	495	Kemba Walker	PG	29	BOS	56	56	1742	378	889	...	181	217	268	48	28	117	91	1145	2019-2020	Y
3192	499	Brad Wanamaker	PG	30	BOS	71	1	1369	162	362	...	122	144	179	61	14	76	133	487	2019-2020	Y
3193	503	Tremont Waters	PG	22	BOS	11	1	119	14	49	...	12	12	16	10	2	15	13	40	2019-2020	Y
3194	511	Grant Williams	PF	21	BOS	69	5	1043	87	211	...	119	178	68	30	36	50	163	237	2019-2020	Y
3195	516	Robert Williams	C	22	BOS	29	1	388	64	88	...	88	128	27	22	35	21	51	150	2019-2020	Y

3196 rows × 32 columns

The query below shows 1491 NBA players are in the Teams made Playoffs and 1705 NBA players are in the Teams that did not make Playoffs for NBA seasons from year 2015-2020

overall_nba_playoffs_stats.Playoff.str.contains("Y").sum()
overall_nba_playoffs_stats.Playoff.str.contains("N").sum()

1491

1705

overall_nba_playoffs_stats.shape

(3196, 32)

Variables created that includes all columns that needed to be dropped because they are not relevant to the data analysis

col_to_drop = ['Rk', 'GS', 'FT', 'FTA', 'FT%', 'ORB', 'DRB', 'TRB', 'AST', 'STL', 'BLK', 'TOV', 'PF']

In the new dataframe named overall_nba_playoffs_stats2 that uses the col_to_drop variable to eliminate all irrelevant 13 columns, and they will be dropped from the table

overall_nba_playoffs_stats2 = overall_nba_playoffs_stats.drop(columns=col_to_drop, inplace=False)
overall_nba_playoffs_stats2

	Player	Pos	Age	Tm	G	MP	FG	FGA	FG%	3P	3PA	3P%	2P	2PA	2P%	eFG%	PTS	Year	Playoff
0	Quincy Acy	PF	25	SAC	59	876	119	214	0.556	19	49	0.388	100	165	0.606	0.600	307	2015-2016	N
1	James Anderson	SG	26	SAC	51	721	67	178	0.376	23	86	0.267	44	92	0.478	0.441	179	2015-2016	N
2	Marco Belinelli	SG	29	SAC	68	1672	245	635	0.386	91	297	0.306	154	338	0.456	0.457	696	2015-2016	N
3	Caron Butler	SF	35	SAC	17	176	25	59	0.424	3	18	0.167	22	41	0.537	0.449	63	2015-2016	N
4	Omri Casspi	PF	27	SAC	69	1880	299	622	0.481	112	274	0.409	187	348	0.537	0.571	813	2015-2016	N
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
3191	Kemba Walker	PG	29	BOS	56	1742	378	889	0.425	180	473	0.381	198	416	0.476	0.526	1145	2019-2020	Y
3192	Brad Wanamaker	PG	30	BOS	71	1369	162	362	0.448	37	102	0.363	125	260	0.481	0.499	487	2019-2020	Y
3193	Tremont Waters	PG	22	BOS	11	119	14	49	0.286	4	24	0.167	10	25	0.400	0.327	40	2019-2020	Y
3194	Grant Williams	PF	21	BOS	69	1043	87	211	0.412	24	96	0.250	63	115	0.548	0.469	237	2019-2020	Y
3195	Robert Williams	C	22	BOS	29	388	64	88	0.727	0	0	NaN	64	88	0.727	0.727	150	2019-2020	Y

3196 rows × 19 columns

Next step, to show shape of the table only 19 remaining from the 32 columns for further analysis

overall_nba_playoffs_stats2.shape

(3196, 19)

Rename columns for better understanding and easier interpretation on what each column means. The variable overall_nba_playoffs_stats2 columns used to rename the main dataframe for the remaining 19 columns. Also successfully confirmed that columns are renamed in the main dataframe

overall_nba_playoffs_stats2_cols = ['Player', 'Position','Age', 'Team','GamesPlayed', 'MinutesPlayed','FieldGoals', 'FieldGoalsAttempts','FieldGoals%', '3Pointers', '3Pointer_Attempts', '3Pointers%', '2Pointers', '2Pointer_Attempts', '2Pointers%', 'EffectiveFieldGoals%', 'TotalPoints', 'Year','Playoff'] 
overall_nba_playoffs_stats2.columns = overall_nba_playoffs_stats2_cols 
overall_nba_playoffs_stats2.columns
overall_nba_playoffs_stats2

Index(['Player', 'Position', 'Age', 'Team', 'GamesPlayed', 'MinutesPlayed',
       'FieldGoals', 'FieldGoalsAttempts', 'FieldGoals%', '3Pointers',
       '3Pointer_Attempts', '3Pointers%', '2Pointers', '2Pointer_Attempts',
       '2Pointers%', 'EffectiveFieldGoals%', 'TotalPoints', 'Year', 'Playoff'],
      dtype='object')

	Player	Position	Age	Team	GamesPlayed	MinutesPlayed	FieldGoals	FieldGoalsAttempts	FieldGoals%	3Pointers	3Pointer_Attempts	3Pointers%	2Pointers	2Pointer_Attempts	2Pointers%	EffectiveFieldGoals%	TotalPoints	Year	Playoff
0	Quincy Acy	PF	25	SAC	59	876	119	214	0.556	19	49	0.388	100	165	0.606	0.600	307	2015-2016	N
1	James Anderson	SG	26	SAC	51	721	67	178	0.376	23	86	0.267	44	92	0.478	0.441	179	2015-2016	N
2	Marco Belinelli	SG	29	SAC	68	1672	245	635	0.386	91	297	0.306	154	338	0.456	0.457	696	2015-2016	N
3	Caron Butler	SF	35	SAC	17	176	25	59	0.424	3	18	0.167	22	41	0.537	0.449	63	2015-2016	N
4	Omri Casspi	PF	27	SAC	69	1880	299	622	0.481	112	274	0.409	187	348	0.537	0.571	813	2015-2016	N
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
3191	Kemba Walker	PG	29	BOS	56	1742	378	889	0.425	180	473	0.381	198	416	0.476	0.526	1145	2019-2020	Y
3192	Brad Wanamaker	PG	30	BOS	71	1369	162	362	0.448	37	102	0.363	125	260	0.481	0.499	487	2019-2020	Y
3193	Tremont Waters	PG	22	BOS	11	119	14	49	0.286	4	24	0.167	10	25	0.400	0.327	40	2019-2020	Y
3194	Grant Williams	PF	21	BOS	69	1043	87	211	0.412	24	96	0.250	63	115	0.548	0.469	237	2019-2020	Y
3195	Robert Williams	C	22	BOS	29	388	64	88	0.727	0	0	NaN	64	88	0.727	0.727	150	2019-2020	Y

3196 rows × 19 columns

I have created 4 extra reference data column with MinutesPerGame, FieldGoalsPerGame, 3PointerPerGame, and 2PointerPerGame to have better understanding and interpretation of the data. Also, would get easier for the viewer to observe Exploratory Data Analysis done on the following columns.

Next step, to show shape of the table only 24 remaining columns for further analysis

overall_nba_playoffs_stats2['MinutesPerGame'] = round(overall_nba_playoffs_stats2['MinutesPlayed'] / overall_nba_playoffs_stats2['GamesPlayed'],2)
overall_nba_playoffs_stats2['FieldGoalsPerGame'] = round(overall_nba_playoffs_stats2['FieldGoals'] / overall_nba_playoffs_stats2['GamesPlayed'],0)
overall_nba_playoffs_stats2['3PointerPerGame'] = round(overall_nba_playoffs_stats2['3Pointers'] / overall_nba_playoffs_stats2['GamesPlayed'], 0)
overall_nba_playoffs_stats2['2PointerPerGame'] = round(overall_nba_playoffs_stats2['2Pointers'] / overall_nba_playoffs_stats2['GamesPlayed'], 0)
overall_nba_playoffs_stats2['TotalPointsPerGame'] = round(overall_nba_playoffs_stats2['TotalPoints'] / overall_nba_playoffs_stats2['GamesPlayed'], 0)

overall_nba_playoffs_stats2

	Player	Position	Age	Team	GamesPlayed	MinutesPlayed	FieldGoals	FieldGoalsAttempts	FieldGoals%	3Pointers	...	2Pointers%	EffectiveFieldGoals%	TotalPoints	Year	Playoff	MinutesPerGame	FieldGoalsPerGame	3PointerPerGame	2PointerPerGame	TotalPointsPerGame
0	Quincy Acy	PF	25	SAC	59	876	119	214	0.556	19	...	0.606	0.600	307	2015-2016	N	14.85	2.0	0.0	2.0	5.0
1	James Anderson	SG	26	SAC	51	721	67	178	0.376	23	...	0.478	0.441	179	2015-2016	N	14.14	1.0	0.0	1.0	4.0
2	Marco Belinelli	SG	29	SAC	68	1672	245	635	0.386	91	...	0.456	0.457	696	2015-2016	N	24.59	4.0	1.0	2.0	10.0
3	Caron Butler	SF	35	SAC	17	176	25	59	0.424	3	...	0.537	0.449	63	2015-2016	N	10.35	1.0	0.0	1.0	4.0
4	Omri Casspi	PF	27	SAC	69	1880	299	622	0.481	112	...	0.537	0.571	813	2015-2016	N	27.25	4.0	2.0	3.0	12.0
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
3191	Kemba Walker	PG	29	BOS	56	1742	378	889	0.425	180	...	0.476	0.526	1145	2019-2020	Y	31.11	7.0	3.0	4.0	20.0
3192	Brad Wanamaker	PG	30	BOS	71	1369	162	362	0.448	37	...	0.481	0.499	487	2019-2020	Y	19.28	2.0	1.0	2.0	7.0
3193	Tremont Waters	PG	22	BOS	11	119	14	49	0.286	4	...	0.400	0.327	40	2019-2020	Y	10.82	1.0	0.0	1.0	4.0
3194	Grant Williams	PF	21	BOS	69	1043	87	211	0.412	24	...	0.548	0.469	237	2019-2020	Y	15.12	1.0	0.0	1.0	3.0
3195	Robert Williams	C	22	BOS	29	388	64	88	0.727	0	...	0.727	0.727	150	2019-2020	Y	13.38	2.0	0.0	2.0	5.0

3196 rows × 24 columns

We have removed all irrelevant columns, and renamed all the remaining columns for easier interpretation. Now we need to identify all missing values in each row from the dataframe so the data can be properly analyzed. To do this step will be using 'overall_nba_playoffs_stats2.isnull().sum()' which used to check for all missing values in each column.

overall_nba_playoffs_stats2.shape 
overall_nba_playoffs_stats2.isnull().sum() #shows all missing values in each column

(3196, 24)

Player                    0
Position                  0
Age                       0
Team                      0
GamesPlayed               0
MinutesPlayed             0
FieldGoals                0
FieldGoalsAttempts        0
FieldGoals%              17
3Pointers                 0
3Pointer_Attempts         0
3Pointers%              249
2Pointers                 0
2Pointer_Attempts         0
2Pointers%               52
EffectiveFieldGoals%     17
TotalPoints               0
Year                      0
Playoff                   0
MinutesPerGame            0
FieldGoalsPerGame         0
3PointerPerGame           0
2PointerPerGame           0
TotalPointsPerGame        0
dtype: int64

For the data clean up of missing values we need to take step-by-step approach rather than using 'dropna'function on entire dataframe because based on NBA players position some players may not shoot 3Pointers vice-versa other players may not shoot 2Pointers which affects percentage for following columns above using missing values. We will create variables for all rows in each column that contain missing values, and update dataframe

All rows with missing values in both 'FieldGoals%' and 'EffectiveFieldGoals%' series removed from overall_nba_playoffs_stats2. The missing values showned for the following means that these haven't scored any points in that season. The amount of Games played were really low could be because of injury.

missing_fieldgoals_percentage = overall_nba_playoffs_stats2[overall_nba_playoffs_stats2['FieldGoals%'].isnull()]
missing_fieldgoals_percentage

	Player	Position	Age	Team	GamesPlayed	MinutesPlayed	FieldGoals	FieldGoalsAttempts	FieldGoals%	3Pointers	...	2Pointers%	EffectiveFieldGoals%	TotalPoints	Year	Playoff	MinutesPerGame	FieldGoalsPerGame	3PointerPerGame	2PointerPerGame	TotalPointsPerGame
38	Jarnell Stokes	C	22	MEM	2	4	0	0	NaN	0	...	NaN	NaN	0	2015-2016	Y	2.00	0.0	0.0	0.0	0.0
219	James Ennis	SF	25	MIA	3	7	0	0	NaN	0	...	NaN	NaN	0	2015-2016	Y	2.33	0.0	0.0	0.0	0.0
324	Sam Dekker	PF	21	HOU	3	6	0	0	NaN	0	...	NaN	NaN	0	2015-2016	Y	2.00	0.0	0.0	0.0	0.0
821	Andrew Bogut	C	32	CLE	1	1	0	0	NaN	0	...	NaN	NaN	0	2016-2017	Y	1.00	0.0	0.0	0.0	0.0
1076	Danuel House	SG	23	WAS	1	1	0	0	NaN	0	...	NaN	NaN	0	2016-2017	Y	1.00	0.0	0.0	0.0	0.0
1207	Rashad Vaughn	SG	21	BRK	1	4	0	0	NaN	0	...	NaN	NaN	0	2017-2018	N	4.00	0.0	0.0	0.0	0.0
1396	Trey McKinney-Jones	SG	27	IND	1	1	0	0	NaN	0	...	NaN	NaN	0	2017-2018	Y	1.00	0.0	0.0	0.0	0.0
1397	Ben Moore	PF	22	IND	2	9	0	0	NaN	0	...	NaN	NaN	0	2017-2018	Y	4.50	0.0	0.0	0.0	0.0
1482	Tyler Lydon	PF	21	DEN	1	2	0	0	NaN	0	...	NaN	NaN	0	2017-2018	N	2.00	0.0	0.0	0.0	0.0
1871	George King	SF	25	PHO	1	6	0	0	NaN	0	...	NaN	NaN	0	2018-2019	N	6.00	0.0	0.0	0.0	0.0
1873	Eric Moreland	PF	27	PHO	1	5	0	0	NaN	0	...	NaN	NaN	0	2018-2019	N	5.00	0.0	0.0	0.0	0.0
1929	John Holland	SF	30	CLE	1	1	0	0	NaN	0	...	NaN	NaN	0	2018-2019	N	1.00	0.0	0.0	0.0	0.0
1941	Kobi Simmons	PG	21	CLE	1	2	0	0	NaN	0	...	NaN	NaN	0	2018-2019	N	2.00	0.0	0.0	0.0	0.0
2001	Tyler Ulis	PG	23	CHI	1	1	0	0	NaN	0	...	NaN	NaN	0	2018-2019	N	1.00	0.0	0.0	0.0	0.0
2255	Ray Spalding	PF	21	DAL	1	1	0	0	NaN	0	...	NaN	NaN	0	2018-2019	N	1.00	0.0	0.0	0.0	0.0
3136	Marques Bolden	C	21	CLE	1	3	0	0	NaN	0	...	NaN	NaN	0	2019-2020	N	3.00	0.0	0.0	0.0	0.0
3146	J.P. Macura	SG	24	CLE	1	1	0	0	NaN	0	...	NaN	NaN	0	2019-2020	N	1.00	0.0	0.0	0.0	0.0

17 rows × 24 columns

overall_nba_playoffs_stats2 = overall_nba_playoffs_stats2.drop(missing_fieldgoals_percentage.index)
overall_nba_playoffs_stats2.isnull().sum() #shows all missing values in each column

Player                    0
Position                  0
Age                       0
Team                      0
GamesPlayed               0
MinutesPlayed             0
FieldGoals                0
FieldGoalsAttempts        0
FieldGoals%               0
3Pointers                 0
3Pointer_Attempts         0
3Pointers%              232
2Pointers                 0
2Pointer_Attempts         0
2Pointers%               35
EffectiveFieldGoals%      0
TotalPoints               0
Year                      0
Playoff                   0
MinutesPerGame            0
FieldGoalsPerGame         0
3PointerPerGame           0
2PointerPerGame           0
TotalPointsPerGame        0
dtype: int64

All rows with missing values in '3Pointers%'and '2Pointers%' series are checked from overall_nba_playoffs_stats2 dataframe. As mentioned earlier that based on NBA players position some players may not shoot 3Pointers vice-versa other players may not shoot 2Pointers which affects percentage for following columns above using missing values.These rows with missing values are not dropped. We will replace all missing value 0 with 'overall_play_nba_playoffs_stats2.fillna(0, inplace=True)' functionality.

overall_nba_playoffs_stats2.shape #used to confirm rows dropped 
overall_nba_playoffs_stats2.isnull().sum() #used to reveal remaining rows with missing values
overall_nba_playoffs_stats2.fillna(0, inplace=True)
overall_nba_playoffs_stats2.isnull().sum() #used to reveal remaining rows with missing values
overall_nba_playoffs_stats2.shape
overall_nba_playoffs_stats2.dtypes

(3179, 24)

Player                    0
Position                  0
Age                       0
Team                      0
GamesPlayed               0
MinutesPlayed             0
FieldGoals                0
FieldGoalsAttempts        0
FieldGoals%               0
3Pointers                 0
3Pointer_Attempts         0
3Pointers%              232
2Pointers                 0
2Pointer_Attempts         0
2Pointers%               35
EffectiveFieldGoals%      0
TotalPoints               0
Year                      0
Playoff                   0
MinutesPerGame            0
FieldGoalsPerGame         0
3PointerPerGame           0
2PointerPerGame           0
TotalPointsPerGame        0
dtype: int64

Player                  0
Position                0
Age                     0
Team                    0
GamesPlayed             0
MinutesPlayed           0
FieldGoals              0
FieldGoalsAttempts      0
FieldGoals%             0
3Pointers               0
3Pointer_Attempts       0
3Pointers%              0
2Pointers               0
2Pointer_Attempts       0
2Pointers%              0
EffectiveFieldGoals%    0
TotalPoints             0
Year                    0
Playoff                 0
MinutesPerGame          0
FieldGoalsPerGame       0
3PointerPerGame         0
2PointerPerGame         0
TotalPointsPerGame      0
dtype: int64

(3179, 24)

Player                   object
Position                 object
Age                       int64
Team                     object
GamesPlayed               int64
MinutesPlayed             int64
FieldGoals                int64
FieldGoalsAttempts        int64
FieldGoals%             float64
3Pointers                 int64
3Pointer_Attempts         int64
3Pointers%              float64
2Pointers                 int64
2Pointer_Attempts         int64
2Pointers%              float64
EffectiveFieldGoals%    float64
TotalPoints               int64
Year                     object
Playoff                  object
MinutesPerGame          float64
FieldGoalsPerGame       float64
3PointerPerGame         float64
2PointerPerGame         float64
TotalPointsPerGame      float64
dtype: object

A boxplot is created for 'GamesPlayed' and 'Age' to reveal any significant outliers.

overall_nba_playoffs_stats2.boxplot(column=['GamesPlayed', 'Age'])
overall_nba_playoffs_stats2[['GamesPlayed', 'Age']].describe(percentiles = [.25, .5, .75, .95])

<matplotlib.axes._subplots.AxesSubplot at 0x11c721e80>

	GamesPlayed	Age
count	3179.000000	3179.000000
mean	44.413023	26.262347
std	26.067750	4.184737
min	1.000000	19.000000
25%	20.000000	23.000000
50%	48.000000	26.000000
75%	68.000000	29.000000
95%	81.000000	34.000000
max	82.000000	43.000000

Explaining Box Plot in GamesPlayed Column: The GamesPlayed column box plot displays the summary of five sets such as the lower whiskers represents minimum games played by NBA player is 1. Similarly, upper whiskers is the most games played by an NBA player is 82 per season. The lower quartile shows 25% of NBA player that played below 20 games. The upper quartile represents 75% of NBA player that played below 68 games. The inter-quartile represents average NBA player games played is 48. There are no significant outliers in the Gamesplayed box plot.

Explaining Box Plot in Age Column: The Age column box plot displays the summary of five sets such as the lower whisker is the youngest NBA player of age 19 in our data. Similarly, upper whiskers is the oldest NBA player's age of 39 in our data. The lower quartile shows 25% of NBA player's age are below the age of 23 in the data. The upper quartile shows 75% of NBA player's age are below the age of 29. The inter-quartile represents average NBA player's age is 26. The outliers in the Age column box plot are plotted as individual dots that are in-line with whiskers for instance we can see that upper extreme for the NBA player's age is 39 beyond that age are outliers which has max age of 43 which will be removed from our data.

shape of dataframe shows 3179 rows before dropping NBA players that have age greater that 39

print("Removing Outliers from Age Column")
overall_nba_playoffs_stats2.shape


ageover39 = overall_nba_playoffs_stats2[overall_nba_playoffs_stats2.Age > 39].index

overall_nba_playoffs_stats2.drop(ageover39, inplace=True) #function that drops all players with age above 39

overall_nba_playoffs_stats2.shape 

Removing Outliers from Age Column

(3179, 24)

(3172, 24)

shape of dataframe shows 3172 rows after dropping NBA players that have age greater that 39

overall_nba_playoffs_stats2['MinutesPerGame'].describe(percentiles = [.25, .5, .75, .95])
overall_nba_playoffs_stats2.boxplot(column=['MinutesPerGame'])

count    3172.000000
mean       19.281917
std         9.042911
min         0.670000
25%        12.180000
50%        18.865000
75%        26.702500
95%        33.894500
max        42.000000
Name: MinutesPerGame, dtype: float64

<matplotlib.axes._subplots.AxesSubplot at 0x11ca05580>

Explaining Box Plot in MinutesPerGame Column: The MinutesPerGame column box plot displays the summary of five sets such as the lower whiskers represents minimum minutes per game played by an NBA player is 0.67 minutes. Similarly, upper whiskers is the maximum minutes per game played by an NBA player is 42 minutes. The lower quartile shows 25% of NBA player that played below 12 minutes 18 seconds per game. The upper quartile represents 75% of NBA player that played below 26 minutes 70 seconds per game. The inter-quartile represents average NBA player minutes per game is 19 minutes 28 seconds. There are no significant outliers in the Gamesplayed box plot.

I was just curious to see which NBA player played lowest minutes per games in the data. I have queried that data below because it did not seem realistics to have such a lowest minutes per game stats in an NBA season

overall_nba_playoffs_stats2.shape 
minutespergame = overall_nba_playoffs_stats2[overall_nba_playoffs_stats2.MinutesPerGame <= 0.67]
minutespergame

(3172, 24)

	Player	Position	Age	Team	GamesPlayed	MinutesPlayed	FieldGoals	FieldGoalsAttempts	FieldGoals%	3Pointers	...	2Pointers%	EffectiveFieldGoals%	TotalPoints	Year	Playoff	MinutesPerGame	FieldGoalsPerGame	3PointerPerGame	2PointerPerGame	TotalPointsPerGame
1847	Donte Grantham	SF	23	OKC	3	2	0	2	0.0	0	...	0.0	0.0	0	2018-2019	Y	0.67	0.0	0.0	0.0	0.0

1 rows × 24 columns

overall_nba_playoffs_stats2['FieldGoalsPerGame'].describe(percentiles = [.25, .5, .75, .95])
overall_nba_playoffs_stats2.boxplot(column=['FieldGoalsPerGame'])

count    3172.000000
mean        3.027743
std         2.123702
min         0.000000
25%         1.000000
50%         3.000000
75%         4.000000
95%         7.000000
max        11.000000
Name: FieldGoalsPerGame, dtype: float64

<matplotlib.axes._subplots.AxesSubplot at 0x11cb0f160>

Explain Box Plot in FieldGoalsPerGame Column: The FieldGoalsPerGame column box plot displays the summary of five sets such as the lower whiskers represents minimum fields goals per game by an NBA player is 0 because these include both three and two pointer shots category statistics in this column based on position of an NBA player they shoot only 2 or 3 pointers. Similarly, upper whiskers is the maximum field goals per game by an NBA player is 8. The lower quartile shows 25% of NBA player's field goals per games are below 1. The upper quartile represents 75% of NBA player's field goals per game are below 4. The inter-quartile corresponds to the average NBA Player Fields Goals Per Game is 3. Even though there are outliers according FieldGoalsPerGame box plot I will consider keeping them because as mentioned above FieldGoals plays a big factor based NBA player's position also doing data analysis on my hypothesis

shape of dataframe is still 3172 rows after analyzing the FieldsGoalsPerGame box plot

overall_nba_playoffs_stats2.shape
overall_nba_playoffs_stats2.boxplot(column=['3PointerPerGame', '2PointerPerGame', 'TotalPointsPerGame'])
overall_nba_playoffs_stats2[['3PointerPerGame', '2PointerPerGame', 'TotalPointsPerGame']].describe(percentiles = [.25, .5, .75, .95])

(3172, 24)

<matplotlib.axes._subplots.AxesSubplot at 0x11cbb0880>

	3PointerPerGame	2PointerPerGame	TotalPointsPerGame
count	3172.000000	3172.000000	3172.000000
mean	0.775851	2.216898	8.228562
std	0.844152	1.765750	5.840660
min	0.000000	0.000000	0.000000
25%	0.000000	1.000000	4.000000
50%	1.000000	2.000000	7.000000
75%	1.000000	3.000000	11.000000
95%	2.000000	6.000000	20.000000
max	5.000000	10.000000	36.000000

Explain Box Plot in 3PointerPerGame Column: The 3PointerPerGame column box plot displays the summary of five sets such as the lower whiskers represents minimum 3 pointers per game by an NBA player is 0 because position of an NBA player they shoot only 2 or 3 pointers. Similarly, upper whiskers is the maximum of 5 three pointers per game scored by an NBA player. The lower quartile shows 25% of NBA player's have scored below 0 three pointers per game for instance NBA position such as 'Center' or 'PowerForward mostly do not shoot 3 pointers in a game. The upper quartile represents 75% of NBA player's have scored below 1 three pointers per game. The inter-quartile corresponds to the average NBA Player score 1 three pointer per game in a regular season.

Explain Box Plot in 2PointerPerGame Column: The 2PointerPerGame column box plot displays the summary of five sets such as the lower whiskers represents minimum 2 pointers per game by an NBA player is 0 because position of an NBA player they shoot only 2 or 3 pointers. Similarly, upper whiskers is the maximum of 10 two pointers per game scored by an NBA player. The lower quartile shows 25% of NBA player's have scored below 1 two pointers per game. The upper quartile represents 75% of NBA player's have scored below 3 two pointers per game. The inter-quartile corresponds to the average NBA Player score 2 two pointers per game in a regular season.

Explain Box Plot in TotalPointsPerGame Column: The TotalPointsPerGame column box plot displays the summary of five sets such as the lower whiskers represents minimum total points per game by an NBA player is 0 because position of an NBA player they shoot only 2 or 3 pointers also there is a chance player could be injured. Similarly, upper whiskers is the maximum of 36 total points per game scored by an NBA player. The lower quartile shows 25% of NBA player's have scored below 4 total points per game. The upper quartile represents 75% of NBA player's have scored below 11 total points per game. The inter-quartile corresponds to the average NBA Player score 7 total points per games in a regular season.

To keep in mind that the following is historical data from the year 2015-2020 NBA seasons where the data can fluctuate in these columns mentioned above. Also, even though there are outliers in the columns above we will keep them because these columns above play a vital role in data analysis that shooting 3 pointers is an optimal solution over 2 pointers to make the NBA playoffs.

We are trying to reorder the columns in the table so that it looks organized for better understanding as the reviewer.

Below, I am showing the before reordering process of the columns where all columns that we are newly added are at the end of the table

overall_nba_playoffs_stats2.columns

Index(['Player', 'Position', 'Age', 'Team', 'GamesPlayed', 'MinutesPlayed',
       'FieldGoals', 'FieldGoalsAttempts', 'FieldGoals%', '3Pointers',
       '3Pointer_Attempts', '3Pointers%', '2Pointers', '2Pointer_Attempts',
       '2Pointers%', 'EffectiveFieldGoals%', 'TotalPoints', 'Year', 'Playoff',
       'MinutesPerGame', 'FieldGoalsPerGame', '3PointerPerGame',
       '2PointerPerGame', 'TotalPointsPerGame'],
      dtype='object')

In this step, we are reordering the newly added columns properly in the table which were added at the end of the table earlier.

overall_nba_playoffs_stats2 = overall_nba_playoffs_stats2[['Player', 'Position', 'Age', 'Team', 'GamesPlayed', 'MinutesPlayed', 'MinutesPerGame', 'FieldGoals', 
                            'FieldGoalsPerGame', 'FieldGoalsAttempts', 'FieldGoals%', '3Pointers', '3PointerPerGame', '3Pointer_Attempts', 
                             '3Pointers%','2Pointers', '2PointerPerGame', '2Pointer_Attempts','2Pointers%', 'EffectiveFieldGoals%', 
                             'TotalPoints', 'TotalPointsPerGame', 'Year','Playoff']]

overall_nba_playoffs_stats2.columns

Index(['Player', 'Position', 'Age', 'Team', 'GamesPlayed', 'MinutesPlayed',
       'MinutesPerGame', 'FieldGoals', 'FieldGoalsPerGame',
       'FieldGoalsAttempts', 'FieldGoals%', '3Pointers', '3PointerPerGame',
       '3Pointer_Attempts', '3Pointers%', '2Pointers', '2PointerPerGame',
       '2Pointer_Attempts', '2Pointers%', 'EffectiveFieldGoals%',
       'TotalPoints', 'TotalPointsPerGame', 'Year', 'Playoff'],
      dtype='object')

In the final step, making sure that shape of the dataframe as the correct amount of rows and columns as expected from the cleanup process. Furthermore made a last check for to make sure there are no nulls values in the columns. There were 3196 rows and 32 columns in the beginning after joining with reference data, then eliminated all 13 irrelevant columns, and added 5 calculated reference columns so that better analysis as well as interpretation of the data. A total of 24 rows were eliminated. After, doing the the cleanup process for phase one 3172 rows and 24 columns will be used for data visualization in the next phase.

overall_nba_playoffs_stats2.shape
overall_nba_playoffs_stats2.isnull().sum()
overall_nba_playoffs_stats2.info()

(3172, 24)

Player                  0
Position                0
Age                     0
Team                    0
GamesPlayed             0
MinutesPlayed           0
MinutesPerGame          0
FieldGoals              0
FieldGoalsPerGame       0
FieldGoalsAttempts      0
FieldGoals%             0
3Pointers               0
3PointerPerGame         0
3Pointer_Attempts       0
3Pointers%              0
2Pointers               0
2PointerPerGame         0
2Pointer_Attempts       0
2Pointers%              0
EffectiveFieldGoals%    0
TotalPoints             0
TotalPointsPerGame      0
Year                    0
Playoff                 0
dtype: int64

<class 'pandas.core.frame.DataFrame'>
Int64Index: 3172 entries, 0 to 3195
Data columns (total 24 columns):
 #   Column                Non-Null Count  Dtype  
---  ------                --------------  -----  
 0   Player                3172 non-null   object 
 1   Position              3172 non-null   object 
 2   Age                   3172 non-null   int64  
 3   Team                  3172 non-null   object 
 4   GamesPlayed           3172 non-null   int64  
 5   MinutesPlayed         3172 non-null   int64  
 6   MinutesPerGame        3172 non-null   float64
 7   FieldGoals            3172 non-null   int64  
 8   FieldGoalsPerGame     3172 non-null   float64
 9   FieldGoalsAttempts    3172 non-null   int64  
 10  FieldGoals%           3172 non-null   float64
 11  3Pointers             3172 non-null   int64  
 12  3PointerPerGame       3172 non-null   float64
 13  3Pointer_Attempts     3172 non-null   int64  
 14  3Pointers%            3172 non-null   float64
 15  2Pointers             3172 non-null   int64  
 16  2PointerPerGame       3172 non-null   float64
 17  2Pointer_Attempts     3172 non-null   int64  
 18  2Pointers%            3172 non-null   float64
 19  EffectiveFieldGoals%  3172 non-null   float64
 20  TotalPoints           3172 non-null   int64  
 21  TotalPointsPerGame    3172 non-null   float64
 22  Year                  3172 non-null   object 
 23  Playoff               3172 non-null   object 
dtypes: float64(9), int64(10), object(5)
memory usage: 619.5+ KB

overall_nba_playoffs_stats2.to_csv('nba_stats_final_phase1.csv', header = True, mode = 'w', index=False)

STOP HERE for your EDA Phase 1 assignment. Submit your cleaned data file along with the completed notebook up to this point for grading.

EDA Phase 2

All of your work for the EDA Phase 2 assignment will begin below here. Refer to the detailed instructions and expectations for this assignment in Canvas.

import pandas as pd
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt
import matplotlib as mpl
import plotly.express as px
%matplotlib inline

from IPython.core.interactiveshell import InteractiveShell
InteractiveShell.ast_node_interactivity = "all"

df = pd.read_csv('nba_stats_final_phase1.csv')
df.info()
df.head()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 3172 entries, 0 to 3171
Data columns (total 24 columns):
 #   Column                Non-Null Count  Dtype  
---  ------                --------------  -----  
 0   Player                3172 non-null   object 
 1   Position              3172 non-null   object 
 2   Age                   3172 non-null   int64  
 3   Team                  3172 non-null   object 
 4   GamesPlayed           3172 non-null   int64  
 5   MinutesPlayed         3172 non-null   int64  
 6   MinutesPerGame        3172 non-null   float64
 7   FieldGoals            3172 non-null   int64  
 8   FieldGoalsPerGame     3172 non-null   float64
 9   FieldGoalsAttempts    3172 non-null   int64  
 10  FieldGoals%           3172 non-null   float64
 11  3Pointers             3172 non-null   int64  
 12  3PointerPerGame       3172 non-null   float64
 13  3Pointer_Attempts     3172 non-null   int64  
 14  3Pointers%            3172 non-null   float64
 15  2Pointers             3172 non-null   int64  
 16  2PointerPerGame       3172 non-null   float64
 17  2Pointer_Attempts     3172 non-null   int64  
 18  2Pointers%            3172 non-null   float64
 19  EffectiveFieldGoals%  3172 non-null   float64
 20  TotalPoints           3172 non-null   int64  
 21  TotalPointsPerGame    3172 non-null   float64
 22  Year                  3172 non-null   object 
 23  Playoff               3172 non-null   object 
dtypes: float64(9), int64(10), object(5)
memory usage: 594.9+ KB

	Player	Position	Age	Team	GamesPlayed	MinutesPlayed	MinutesPerGame	FieldGoals	FieldGoalsPerGame	FieldGoalsAttempts	...	3Pointers%	2Pointers	2PointerPerGame	2Pointer_Attempts	2Pointers%	EffectiveFieldGoals%	TotalPoints	TotalPointsPerGame	Year	Playoff
0	Quincy Acy	PF	25	SAC	59	876	14.85	119	2.0	214	...	0.388	100	2.0	165	0.606	0.600	307	5.0	2015-2016	N
1	James Anderson	SG	26	SAC	51	721	14.14	67	1.0	178	...	0.267	44	1.0	92	0.478	0.441	179	4.0	2015-2016	N
2	Marco Belinelli	SG	29	SAC	68	1672	24.59	245	4.0	635	...	0.306	154	2.0	338	0.456	0.457	696	10.0	2015-2016	N
3	Caron Butler	SF	35	SAC	17	176	10.35	25	1.0	59	...	0.167	22	1.0	41	0.537	0.449	63	4.0	2015-2016	N
4	Omri Casspi	PF	27	SAC	69	1880	27.25	299	4.0	622	...	0.409	187	3.0	348	0.537	0.571	813	12.0	2015-2016	N

5 rows × 24 columns

NBA Statistics Heatmap Analysis

columns = ['TotalPointsPerGame','FieldGoalsPerGame', '3PointerPerGame', '2PointerPerGame', 'MinutesPerGame']
df_corr = df[columns]
# setting up the heatmap
corrmat = df_corr.corr()

# set the figure size
f, ax = plt.subplots(figsize=(9, 6))
# TODO create a heat map using all six numeric variables. Pick a new color combination.
# https://matplotlib.org/3.1.1/gallery/color/colormap_reference.html
sns.heatmap(corrmat, vmax=.8, square=True, annot=True, cmap='RdYlGn', linewidths=.5 )
plt.title('Heatmap NBA Statistics Analysis')
#TODO explain how the visual cues of the heatmap represent the correlactions.
plt.savefig('Correlation Heat Map Beer Reviews')

<matplotlib.axes._subplots.AxesSubplot at 0x11f850d90>

Text(0.5, 1.0, 'Heatmap NBA Statistics Analysis')

Explaination of how the visual cues of the heatmap represent the correlations.

TotalPointsPerGame Correlations Analysis:

Based on correlation matrix the dark green color shows high positive correlation between Total points per game and three other columns such as FieldGoals, 2 pointer, and minutes played by an NBA player per game. This relationship definitely makes sense because when we observe an NBA overall Total points per game it depends upon these columns in terms the amount of minutes played by a player, fields goals attempts in 2 pointer or 3 pointer shooting scores category by the player based on that the Most Valuable Player of the Game is awarded. Similarly, light green color show slight positive correlation as the amount of 3 pointers scored by an NBA player might be less based on player's position.

FieldGoalsPerGame Correlations Analysis:

In the Field goals per game correlation matrix as mentioned dark green shows high positive correlation between Field Goals per game and other three columns such as Total points, 2 pointer, and Minutes per game. Similar to analysis of total points the field goals also depends how many 2 pointers player attempted and scored points which gets tallies to the total points, and amount of minutes played is also important for this analysis. The yellow color shows moderate neutral correlation between fields goals and 3 pointers per games because as field goals included both 2 and 3 pointers shooting category there are two possibilities either player might have attempted more 2 pointers per games or player missed a lot of 3 pointers per game during the season.

3PointerPerGame Correlations with TotalPointsPerGame, FieldGoalsPerGame and MinutesPerGame Analysis:

The three pointer per game shows high positive relationship in dark green color with total points and minutes per game because its dependent on how minutes player as played also three pointer shooting score gets tallied to overall total points per game. On the other hand, three pointer per game shows moderate neutral correlation with field goals per game because players may have not been scoring based on the amount of fields goals they have attempted per game.

2PointerPerGame Correlations with TotalPointsPerGame, FieldGoalPerGame and MinutesPerGame Analysis:

The two pointer per game shows high positive relationship in dark green color with total points and fields goals per game because players might have been scoring around range or amount of fields goals they attempted per game. Also, two pointer shooting score gets tallied to overall total points per game. The minutes per game shows medium positive relationship because its dependent on how many minutes player have played which could affect their shooting category either in a positive or negative way.

3PointerPerGame Correlations with 2PointerPerGame Analysis:

The three pointer and two pointer per games shows strong negative correlation in red color as they both are two different shooting category and not dependent on each other in terms of contribution towards player's overall statistics.

Seaborn Box Plot Analysis

f, axes = plt.subplots(1,4)
plt.figsize=(10,20)
sns.boxplot(df['GamesPlayed'], orient = 'v', color = 'forestgreen', ax = axes[0])
sns.boxplot(df['Age'], orient = 'v', color = 'darkorange', ax = axes[1])
sns.boxplot(df['MinutesPerGame'], orient = 'v', color = 'lightgray', ax = axes[2])
sns.boxplot(df['FieldGoalsPerGame'], orient = 'v', color = 'dimgrey', ax = axes[3])
plt.tight_layout()


f, axes = plt.subplots(1,3)
plt.figsize=(10,20)
sns.boxplot(df['2PointerPerGame'], orient = 'v', color = 'darkviolet', ax = axes[0])
sns.boxplot(df['3PointerPerGame'], orient = 'v', color = 'darksalmon', ax = axes[1])
sns.boxplot(df['TotalPointsPerGame'], orient = 'v', color = 'lavender', ax = axes[2])
plt.tight_layout()

<matplotlib.axes._subplots.AxesSubplot at 0x11fd35c70>

<matplotlib.axes._subplots.AxesSubplot at 0x11fd58d60>

<matplotlib.axes._subplots.AxesSubplot at 0x11fd8b1c0>

<matplotlib.axes._subplots.AxesSubplot at 0x11fdb65e0>

<matplotlib.axes._subplots.AxesSubplot at 0x11fe6dfa0>

<matplotlib.axes._subplots.AxesSubplot at 0x11fe9a5b0>

<matplotlib.axes._subplots.AxesSubplot at 0x11fec39d0>

Explain Outliers: GamesPlayed, Age, MinutesPerGame and FieldGoalsPerGame

There are no significant outliers in the Gamesplayed box plot. The outliers in the Age column box plot are accurate we can see that upper extreme for the NBA player's age is 38 but beyond that are outliers based on data there are several players who are still in NBA around that age range of 40 and are in the team that make playoffs.There are no significant outliers in the MinutesPerGame box plot. The FieldsPerGame box plot shows accurate outliers because every season produces a group of players who achieve superior offensive statistics scoring around range of 9 to 11 fieldsgoals per game. Also, FieldsPerGame include both three and two pointer shots category statistics in this column based on position of an NBA player they shoot only 2 or 3 pointers.

Explain Outliers: 2PointerPerGame, 3PointerPerGame, and TotalPointsPerGame

The outliers for all the following columns are accurate as represented by box plot. The outliers for 2PointerPerGame lies beyond the upper extreme is around 7 to 10 two pointer per game. Similarly, for 3PointerPerGame box plot indicates NBA players shooting around the range of 3 to 5 three pointers per game are outliers. Finally, TotalPointsPerGame box plot looks kind of accurate based on our data because some players might be injured or have played less minutes per games compare to other which skews the box plot upper extreme of NBA players scoring greater than 22 points per games are outliers.

But the overwhelming majority of players who typically achieve subpar offensive statistics in following shooting category play a vital roles in our analysis because these outliers are NBA players leading in average two pointer, three pointer and total points per game category doing analysis on these outliers would help us in understanding and answering our hypothesis

Swarm Plot Analysis 1: Based on NBA Player's Position

plt.figure(figsize=(10, 6))
sns.swarmplot(x=df['3PointerPerGame'], y=df['TotalPointsPerGame'], palette='Spectral', hue=df['Position'])
plt.title("TotalPointsPerGame vs 3PointerPerGame", size=15)
plt.legend(bbox_to_anchor=(1.0, 1), loc=2, borderaxespad=1)

<Figure size 720x432 with 0 Axes>

<matplotlib.axes._subplots.AxesSubplot at 0x11ff9a1f0>

Text(0.5, 1.0, 'TotalPointsPerGame vs 3PointerPerGame')

<matplotlib.legend.Legend at 0x11ff95d90>

NBA Player Position
1. PF - Power Forwards
2. SG - Shooting Guards
3. SF - Small Forwards
3. C - Center
4. PG - Point Guards

Explaination of Swarm Plot

The swarm plot is more visible than a scatter plot and are effectively categorized like a bar plot. As we are comparing total points per game scored by NBA player compared to three pointer per game with help of swarm we are able to categorize how different NBA player's position have scored more three pointer per game in our historical data. Based on the swarm plot the red dots in the data corresponds NBA Shooting Guard (SG) position it shows that players in this position have an high average scoring range around 2 to 4 three pointer per game which leads them to having an average of 10 to 25 total points per games compare to other positions which have low scoring range from 0 to 1 three pointer per game

The light orange dots in the data corresponds to NBA Point Guard (PG) position which indicates that players in this position have an high average scoring range around 3 to 5 three pointer per game which leads them to having an average of 10 to 35 total points per games shooting three pointer compare to other positions.

It is interesting to note that players who play multiple positions (e.g., PF-C, SF-SG, PG-SG, PF-SF) do not make significant amount of total points per game. This shows that the players who are assigned multiple positions may have other unique responsibilities compared to traditionally NBA positions mentioned above.

Swarm Plot Analysis 2: Based on NBA Player's Position

plt.figure(figsize=(10, 6))
sns.swarmplot(x=df['2PointerPerGame'], y=df['TotalPointsPerGame'], palette='terrain', hue=df['Position'])
plt.title("TotalPointsPerGame vs 2PointerPerGame", size=15)
plt.legend(bbox_to_anchor=(1.0, 1), loc=2, borderaxespad=1)

<Figure size 720x432 with 0 Axes>

<matplotlib.axes._subplots.AxesSubplot at 0x11ff95ee0>

Text(0.5, 1.0, 'TotalPointsPerGame vs 2PointerPerGame')

<matplotlib.legend.Legend at 0x11fed1670>

NBA Player Position
1. PF - Power Forwards
2. SG - Shooting Guards
3. SF - Small Forwards
3. C - Center
4. PG - Point Guards

Explaination of Swarm Plot

Based on the swarm plot the green and light green dots in the data corresponds to NBA Center (C) and Point Guard (PG) position which indicates that players in this position have an high average scoring range around 2 to 7 two pointer per game which leads them to having an average of 5 to 30 total points per games shooting two pointer compare to other positions.

The blue dots in the data corresponds NBA Power Forward (PF) position it shows that players in this position have an high average scoring range around 1 to 6 two pointer per game which leads them to having an average of 5 to 20 total points per games shooting two pointer compare to other positions. Also, it interesting to note that players who play multiple positions (e.g., PF-C, SF-SG, PG-SG, PF-SF) do not make significant amount of total points per game because they might be only be playing multiple positions sometimes during a season, not regularly compare to their normal position.

Pie Chart Analyis: Total 3 Pointer for Overall Season from 2015-2020 Categorized NBA Player Positions

import plotly.graph_objs as go
fig = px.pie(df, values='3Pointers', names='Position',
             title='NBA Players Total 3 Pointers Statistics Season 2015-2020',
             hover_data=['Position'], labels={'3Pointers'})
fig.update_traces(textposition='inside', textinfo='percent+label')

Hover over Data on NBA Player Position
1. PF - Power Forwards
2. SG - Shooting Guards
3. SF - Small Forwards
3. C - Center
4. PG - Point Guards

Explaination of Pie Chart: NBA Player 3 Pointers Statistics Per Season from 2015-2020 Categorized by Positions

To further confirm my hunch either correct or incorrect that the overall data will reveal positive correlation between NBA players in Point Guard (PG) and Shooting Guard (SG) position are likely to score an average of higher three pointers compared to other position. We decided to look at broader data with help of pie chart which represents the overall NBA players 3 pointers statistics per season from year 2015 to 2020. It is sliced into percentages of different NBA positions based on their shootings score per season.

Shooting Guard (SG) and Point Guard (PG) Analysis:
As we can see that 31.6% of the 3 pointers are scored by players in the Shooting Guard (SG) position with the total of 43,471 three pointers from our historical data. Similarly, 22.5% of 3 pointers are scored by NBA players in Point Guard (PG) position with total of 30,924 three pointers for five regular season. It clearly shows that Shooting Guard position have always scored more three pointers per season then any other position in NBA

Small Forwards (SF) and Power Forwards (PF) Analysis:
On the other hand, NBA players playing in Small Forward (SF) have scored 27,797 total three pointers per season,as they cover 20.2% of the data. The pie chart also indicates that 18.3% of 3 pointers per season are scored by players in the Power Forwards position. It is surprising to see there are is not much difference in between Small Forwards, Power Forwards, and Point Guards positions shooting three pointer category. It leads to the fact that NBA player's position doesn't really dependent upon their scoring points style. As shown in the pie chart with three pointer per season data in this case players might trying to learn how to improve in both category over the years which led to us seeing closer percentages.

Center (C) and Multiple Position Analysis:
Center (C) and other multiple positions have combined percentage of only 7.4% in the three pointer per season shooting category for five year historical data which shows they are outliers based on our assumptions they are more likely to attempt more two pointers than three pointers per season. Also, players with multiple positions do not make significant amount of three pointers per season because they might be only be playing multiple positions only sometimes during a season, not regularly compare to their normal position.

Pie Chart Analysis Total 2 Pointer for Overall Season from 2015-2020 Categorized By NBA Player Positions

import plotly.express as px
fig = px.pie(df, values='2Pointers', names='Position',
             title='NBA Players Total 2 Pointers Statistics Season 2015-2020',
             hover_data=['Position'], labels={'2Pointers'})
fig.update_traces(textposition='inside', textinfo='percent+label')

Hover over Data on NBA Player Position
1. PF - Power Forwards
2. SG - Shooting Guards
3. SF - Small Forwards
3. C - Center
4. PG - Point Guards

Explaination of Pie Chart: NBA Player 2 Pointers Statistics Per Season from 2015-2020 Categorized by Positions

To further confirm my hunch either correct or incorrect that the overall data will reveal positive correlation between NBA players in Power Forward (PF), Small Forward (SF), and Center (C) position are likely to score an average of higher two pointers compared to other position. We decided to look at broader data with help of pie chart which represents the overall NBA players 2 pointers statistics per season from year 2015 to 2020. It is sliced into percentages of different NBA positions based on their shootings score per season.

Center (C) and Point Guard (PG) Analysis:
As we can see that 24.2% of the 2 pointers are scored by players in the Center (C) position with the total of 92,408 two pointers from our historical data. Similarly, 20.3% of 2 pointers are scored by NBA players in Point Guard (PG) position with total of 77,449 two pointers for five regular season. It clearly shows that Center position have always scored more two pointers per season then any other position in NBA

Power Forwards (SF) and Shooting Guards (SG) Analysis:
On the other hand, NBA players playing in Power Forward (PF) have scored 74,804 total two pointers per season,as they cover 19.6% of the data. The pie chart also indicates that 19.6% of 2 pointers per season are scored by players in the Shooting Guard (SG) position. It is surprising to see there is tie between Power Forwards and Shooting Guard position in the shooting two pointer category. It leads to the fact that NBA player's position doesn't really dependent upon their scoring points style. As shown in the pie chart with two pointer per season data in this case players might be trying to learn how to improve in both category over the years which led to us seeing tie in terms of percentages between these two NBA positions.

Small Forwards (SF) and Multiple Position Analysis:
Small Forwards (SF) and other multiple positions have combined percentage of only 16.3% in the two pointer per season shooting category for five year historical data which shows they are outliers based on our hunch was incorrect we thought they are more likely to attempt more two pointers than three pointers per season which shows that there is neutral correlation between NBA player's position and the offensive shooting category. The multiple positions only contributed 0.7% of data which indicates that they do not make significant amount of two pointers per season because they might be only be playing multiple positions only sometimes during a season, not regularly compare to their normal position.

Polar Line Plot Analysis :

Total 3 Pointer Per Game for Overall Season from 2015-2020 Categorized By Players Made Playoffs or Not

import plotly.express as px
fig = px.line_polar(df, r='3PointerPerGame', theta='Year',color='Player', hover_name='Playoff', line_close=True, width=800, height=500)
fig.show()

Hover over Year Angle for Leading Scorers in 3 Pointer Per Game Category
1. 2015-2016 shows green line
2. 2016-2017 shows green line
3. 2017-2018 shows green and pink line
3. 2018-2019 shows green and pink line
5. 2019-2020 represents orange line

Polar Line Plot Analysis:
The polar line plot will helps us to understand which NBA players is leading in the three pointer per game category from the year 2015 to 2020 based on the player's data line which shows the amount of three pointer they have scored thats touches particular year angle. Also, retrieve information if the NBA player leading in points made to the playoffs or not.

Let's starts with the regular season year 2015-2016 and 2016-2017 as we hover over the green line on these year column angle we can see that Steph Curry is leading in three pointer category for both these years, and have made the playoffs.

Similarly, there is tie between James Harden indicated in the pink line , and Steph Curry data is in green line as they both touch 2017-2018 and 2018-2019 year angle with leading score of 4 three pointer and 5 three pointer per games for those years.

Finally, Damian Lillard indicated with orange line being a leading scorer with 4 three pointer per game for the year 2019-2020. The data reveals that all the 3 NBA players who are the leading points scorers in the three pointer category have made playoffs. To further confirm our assumptions above will have to look at 2 pointer per game category as well, and see this pattern persist.

Total 2 Pointer Per Game for Overall Season from 2015-2020 Categorized By Players Made Playoffs or Not

import plotly.express as px
fig = px.line_polar(df, r='2PointerPerGame', theta='Year',color='Player', hover_name='Playoff', line_close=True, width=800, height=500)
fig.show()

Hover over Year Angle for Leading Scorers in 2 Pointer Per Game Category
1. 2015-2016 shows red and orange line
2. 2016-2017 shows red line
2. 2017-2018 shows red line
3. 2018-2019 shows red and blue line
5. 2019-2020 represents pink line

Polar Line Plot Analysis:
The polar line plot will helps us to understand which NBA players is leading in the two pointer per game category from the year 2015 to 2020 based on the player's data line which shows the amount of two pointer they have scored thats touches particular year angle. Also, retrieve information if the NBA player leading in points made to the playoffs or not.

Let's starts with the regular season year 2015-2016,2016-2017, 2017-2018, and 2018-2019 as we hover over the red line on these year column angle we can see that Anthony Davisis leading in two pointer category for these four years, but have only made it to the playoffs for once out of 4 years.

In addition, there is a tie between LaMarcus Aldridge indicated in the orange line, and Anthony Davis data is in red line as they both touch 2015-2016 year angle with leading score of 9 two pointer per games for this year but only LaMarcus Aldridge made the playoffs that year, and not Anthony Davis.

Similarly, there is a tie between Giannis Antetokounmpo indicated in the blue line, and Anthony Davis data is in red line as they both touch 2018-2019 year angle with leading score of 9 two pointer per games for this year but only Giannis Antetokounmpo made the playoffs that year, and not Anthony Davis.

Finally, Russell Westbrook indicated with pink line being a leading scorer with 10 two pointer per game for the year 2019-2020. The data reveals that that only 2 out of 3 NBA players who are the leading points scorers in the two pointer category have made playoffs. So, our assumption was wrong the pattern from previous polar plot did not persist, regarding NBA players leading in the shooting category always make the playoffs.

Scatter Plot Analysis: GamesPlayed vs MinutesPerGame from 2015-2020

import plotly.express as px
fig = px.scatter(
    df, x='MinutesPerGame', y='GamesPlayed', size='TotalPoints', size_max=13, color_continuous_scale='rdylbu_r',
    color='Playoff', hover_name='Player', trendline="ols", title='GamesPlayed vs MinutesPerGame')
fig.update_traces(marker=dict(line=dict(width=1, color='DarkSlateGrey')),
                  selector=dict(mode='markers'))

Hover Over Data For Addition Information
1. Player Name
2. Total Points Scored from 2015-2020
4. Games Played
5. Minutes Per Game
6. N - Did not make Playoffs (Blue Dots)
7. Y - Made it to the Playoffs (Red Dots)

Scatter Plot Analysis:
The main goal of this scatter plot is to analyze if the data indicates any correlation between GamesPlayed and MinutesPerGame. Also, how does it affect players chances of making playoffs or not.

As we look at the trendline in the scatter plot we see that a lot of red dots in the right upper quadrant of the plot where NBA players who have played around range of 75 - 80 games and have an average of greater than 30 minutes per game have more chances of making playoffs, and scoring more points based on the historical data.

On the other hand, trendline for the blue dots starts at the lower left quadrant to mid upper right quadrant of the plot where NBA who have played around range of 20 - 65 games and have an average of less than 30 minutes per game have less chances of making playoffs, and scoring points based on the historical data.

But there are some exceptions in the scatter plot for instance there are blue dots in the right upper quadrant of the plot where some NBA players have either played all 82 games or above 75 games, and have an average playing time of more than 30 minutes per games but still not make playoffs. So based on our analysis we can conclude that there is a neutral correlation between GamesPlayed and MinutesPerGame with a dependencies on overall Team statistics with would affect players chances of making playoffs.

Heat Analysis: On Individual NBA Player Statistics based on Average 3 Pointer Per Game

Since the 3PointerPerGame shooting category was highly correlated with overall players statistics we will use 3PointerPerGame score for our analysis to find top 20 players who have a high average in the three pointer shooting category. Based on the following heat map data analysis it would help us reveal that either our hunch is correct or incorrect regarding NBA players who have been scoring an average of high 3-pointers per game and are more likely to be in teams that make the NBA playoffs.

Let's look at top 50 individual NBA players statistics grouped in four different categories by Team, position, playoff, year, and sorted on average of 3 pointers per game

# look at heat map analysis on overall mean 3 Pointer Per Game for five-year historical data

nba_3Pointer =df.groupby(['Player','Team','Position','Playoff','Year']).agg({'3PointerPerGame':['mean']})
nba_3Pointer.columns = ['3PointerPerGame_Mean']
players_avg_3PointerPerGame = nba_3Pointer.sort_values(by=['3PointerPerGame_Mean'], ascending = False)[:50]
players_avg_3PointerPerGame.style.background_gradient(cmap = 'Blues')

					3PointerPerGame_Mean
Player	Team	Position	Playoff	Year
Stephen Curry	GSW	PG	Y	2018-2019	5.000000
				2015-2016	5.000000
James Harden	HOU	PG	Y	2018-2019	5.000000
Paul George	OKC	SF	Y	2018-2019	4.000000
Malik Beasley	MIN	SG	N	2019-2020	4.000000
James Harden	HOU	SG	Y	2017-2018	4.000000
				2019-2020	4.000000
Stephen Curry	GSW	PG	Y	2017-2018	4.000000
				2016-2017	4.000000
Dāvis Bertāns	WAS	PF	N	2019-2020	4.000000
D'Angelo Russell	TOT	PG	N	2019-2020	4.000000
	GSW	PG	N	2019-2020	4.000000
Damian Lillard	POR	PG	Y	2019-2020	4.000000
Buddy Hield	SAC	SG	N	2019-2020	4.000000
R.J. Hunter	BOS	SG	Y	2018-2019	4.000000
Duncan Robinson	MIA	SG	Y	2019-2020	4.000000
Trevor Ariza	HOU	SF	Y	2017-2018	3.000000
Tim Hardaway Jr.	DAL	SG	Y	2019-2020	3.000000
James Harden	HOU	SG	Y	2015-2016	3.000000
Eric Gordon	HOU	SG	Y	2017-2018	3.000000
				2018-2019	3.000000
Allen Crabbe	BRK	SG	N	2017-2018	3.000000
Fred VanVleet	TOR	SG	Y	2019-2020	3.000000
Eric Gordon	NOP	SG	N	2015-2016	3.000000
Danilo Gallinari	OKC	PF	Y	2019-2020	3.000000
Marcus Morris	NYK	SF	N	2019-2020	3.000000
Landry Shamet	LAC	SG	Y	2018-2019	3.000000
Jaren Jackson Jr.	MEM	C	N	2019-2020	3.000000
Kevin Durant	GSW	SF	Y	2017-2018	3.000000
Robert Covington	PHI	SF	Y	2017-2018	3.000000
Tim Hardaway Jr.	NYK	SG	N	2018-2019	3.000000
Bradley Beal	WAS	SG	Y	2016-2017	3.000000
Nick Young	LAL	SG	N	2016-2017	3.000000
Kevin Durant	OKC	SF	Y	2015-2016	3.000000
Eric Gordon	HOU	SG	Y	2016-2017	3.000000
Bogdan Bogdanović	SAC	SG	N	2019-2020	3.000000
Kevin Love	CLE	PF	N	2019-2020	3.000000
Bojan Bogdanović	UTA	SF	Y	2019-2020	3.000000
Bradley Beal	WAS	SG	N	2019-2020	3.000000
Robert Covington	PHI	SF	N	2015-2016	3.000000
Devonte' Graham	CHO	PG	N	2019-2020	3.000000
Justin Holiday	CHI	SG	N	2018-2019	3.000000
Damian Lillard	POR	PG	Y	2018-2019	3.000000
Kemba Walker	CHO	PG	N	2017-2018	3.000000
James Ennis	NOP	SF	N	2015-2016	3.000000
Wayne Ellington	TOT	SG	N	2018-2019	3.000000
Karl-Anthony Towns	MIN	C	N	2019-2020	3.000000
Luka Dončić	DAL	PG	Y	2019-2020	3.000000
Otto Porter	CHI	SF	N	2018-2019	3.000000
Trae Young	ATL	PG	N	2019-2020	3.000000

Explaination of Heat Map Analysis: Top 50 NBA Player Statistics based on Average 3 Pointer Per Game

In the top 50 NBA player statistics 5 three pointer per game is the highest average points in the 3 pointer shooting category scored by Steph Curry and James Harden also they both were in teams that mades the playoffs, and have been consistent in their performance over the years based on the data.

But if we look at the broader picture in the heat map data analysis even though there are some players that appear twice in the data above because either they played for different teams or their consistently performing well in multliple position in different year scoring a high average of three pointer per games. But the main point is there are 23 out of 50 NBA player were not teams that made the playoffs which 46% of the data which concludes that are hunch is incorrect regarding players scoring an average of high 3-pointers per game and are more likely to be in teams that make the NBA playoffs. To further prove our assumptions on this heat map analysis we will narrow the data down to top 10 NBA players based on average 3 pointers per game

Top 10 NBA Player Statistics based on Average 3 Pointer Per Game

players_avg_3PointerPerGame.sort_values(by=['3PointerPerGame_Mean'], ascending = False)[:10]

					3PointerPerGame_Mean
Player	Team	Position	Playoff	Year
Stephen Curry	GSW	PG	Y	2018-2019	5.0
James Harden	HOU	PG	Y	2018-2019	5.0
Stephen Curry	GSW	PG	Y	2015-2016	5.0
Dāvis Bertāns	WAS	PF	N	2019-2020	4.0
Duncan Robinson	MIA	SG	Y	2019-2020	4.0
R.J. Hunter	BOS	SG	Y	2018-2019	4.0
Buddy Hield	SAC	SG	N	2019-2020	4.0
Damian Lillard	POR	PG	Y	2019-2020	4.0
D'Angelo Russell	TOT	PG	N	2019-2020	4.0
	GSW	PG	N	2019-2020	4.0

Explaination of Heat Map Analysis: Top 10 NBA Player Statistics based on Average 3 Pointer Per Game

Now, that we have narrowed our data down to top 10 NBA players having a high average of 3 pointer per game we can see that some of the players have been consistently performing well in their individual statistics shooting like Steph Curry leading in points in the year 2018 and 2015, also being in teams that playoffs. Similarly, 'James Harden' have tied shooting average of 5 three pointers per game.

On the other hand, there are players for instance D'Angelo Russell who has been scoring consistently with 4 three pointer per game in the year '2019' for two different teams but did not make the playoff. In addition, the data reveals that 4 out of 10 players in the leading three points shooting category were not in the teams that made the playoffs which is 40% of the data above. It further proves that individual players statistics do not correlate to their chances of always making the playoffs or being in the teams that make the playoffs.

Heat Analysis: On Individual NBA Player Statistics based on Average 2 Pointer Per Game

We should now analyze 2PointerPerGame shooting category with overall players statistics to see if the data reveals similar patterns in comparison three pointer shooting category. We will use 2PointerPerGame score for our analysis to find top 50 players who have a high average in the two pointer shooting category. Based on the following heat map data analysis it would help us reveal that either our hunch is correct or incorrect regarding NBA players who have been scoring an average of high 2-pointers per game and are more likely to be in teams that make the NBA playoffs.

Let's look at top 50 individual NBA players statistics grouped in four different categories by Team, position, playoff, year, and sorted on average of 2 pointers per game

# look at heat map analysis on overall mean 2 Pointer Per Game for five-year historical data
nba_2Pointer =df.groupby(['Player','Team','Position','Playoff','Year']).agg({'2PointerPerGame':['mean']})
nba_2Pointer.columns = ['2PointerPerGame_Mean']

players_avg_2PointerPerGame = nba_2Pointer.sort_values(by=['2PointerPerGame_Mean'], ascending = False)[:50]
players_avg_2PointerPerGame.style.background_gradient(cmap = 'RdYlGn')

					2PointerPerGame_Mean
Player	Team	Position	Playoff	Year
Anthony Davis	NOP	PF	Y	2017-2018	10.000000
Russell Westbrook	HOU	PG	Y	2019-2020	10.000000
Anthony Davis	NOP	C	N	2016-2017	10.000000
LeBron James	CLE	SF	Y	2015-2016	9.000000
Giannis Antetokounmpo	MIL	PF	Y	2019-2020	9.000000
LeBron James	CLE	PF	Y	2017-2018	9.000000
DeMar DeRozan	TOR	SG	Y	2016-2017	9.000000
Anthony Davis	NOP	C	N	2015-2016	9.000000
				2018-2019	9.000000
Karl-Anthony Towns	MIN	C	N	2016-2017	9.000000
Giannis Antetokounmpo	MIL	PF	Y	2017-2018	9.000000
				2018-2019	9.000000
LaMarcus Aldridge	SAS	C	Y	2017-2018	9.000000
Nikola Vučević	ORL	C	N	2015-2016	8.000000
Russell Westbrook	OKC	PG	Y	2017-2018	8.000000
DeMar DeRozan	SAS	SF	N	2019-2020	8.000000
Joel Embiid	PHI	C	Y	2018-2019	8.000000
DeMarcus Cousins	SAC	C	N	2015-2016	8.000000
LeBron James	LAL	SF	N	2018-2019	8.000000
Zion Williamson	NOP	PF	N	2019-2020	8.000000
Anthony Davis	LAL	PF	Y	2019-2020	8.000000
DeMar DeRozan	SAS	SG	Y	2018-2019	8.000000
Nikola Vučević	ORL	C	Y	2018-2019	8.000000
Giannis Antetokounmpo	MIL	SF	Y	2016-2017	8.000000
Blake Griffin	LAC	PF	Y	2015-2016	8.000000
Russell Westbrook	OKC	PG	Y	2016-2017	8.000000
LeBron James	CLE	SF	Y	2016-2017	8.000000
Deandre Ayton	PHO	C	N	2019-2020	8.000000
LaMarcus Aldridge	SAS	C	Y	2018-2019	8.000000
Brook Lopez	BRK	C	N	2015-2016	8.000000
Jonas Valančiūnas	MEM	C	N	2018-2019	8.000000
T.J. Warren	PHO	SF	N	2017-2018	8.000000
Andre Drummond	DET	C	Y	2015-2016	7.000000
Kevin Durant	GSW	SF	Y	2018-2019	7.000000
Harrison Barnes	DAL	PF	N	2016-2017	7.000000
Andre Drummond	TOT	C	N	2019-2020	7.000000
	DET	C	Y	2018-2019	7.000000
Kevin Durant	GSW	SF	Y	2017-2018	7.000000
Andre Drummond	DET	C	N	2019-2020	7.000000
	CLE	C	N	2019-2020	7.000000
Deandre Ayton	PHO	C	N	2018-2019	7.000000
Blake Griffin	LAC	PF	Y	2016-2017	7.000000
Kyrie Irving	BRK	PG	Y	2019-2020	7.000000
Domantas Sabonis	IND	PF	Y	2019-2020	7.000000
John Wall	WAS	PG	Y	2016-2017	7.000000
Kevin Durant	GSW	PF	Y	2016-2017	7.000000
Jabari Parker	MIL	PF	Y	2016-2017	7.000000
Kyrie Irving	CLE	PG	Y	2016-2017	7.000000
Karl-Anthony Towns	MIN	C	N	2015-2016	7.000000
LaMarcus Aldridge	SAS	PF	Y	2015-2016	7.000000

Explaination of Heat Map Analysis: Top 50 NBA Player Statistics based on Average 2 Pointer Per Game

In the top 50 NBA player statistics 10 three pointer per game is the highest average points in the 2 pointer shooting category scored by Anthony Davis and Russell Westbrook also they both were in teams that mades the playoffs, and have been consistent in their performance over the years based on the data. But at the same time Anthony Davis has also not made in the playoffs while playing for the same team in different seasons. It shows that having good performance in the 2 pointer shooting category does not depend upon guarantee playoffs spot.

But if we look at the broader picture in the heat map data analysis even though there are some players like Anthony Davis, LeBron James,and Russell Westbrook that appear twice in the data above because either they played for different teams or their consistently performing in well in multiple position in different years scoring a high average of two pointer per games. But the main point is there are 18 out of 50 NBA player were not teams that made the playoffs which 36% of the data which concludes that are hunch is incorrect regarding players scoring an average of high 2-pointers per game and are more likely to be in teams that make the NBA playoffs. To further prove our assumptions on this heat map analysis we will narrow the data down to top 10 NBA players based on average 2 pointers per game

Top 10 NBA Player Statistics based on Average 2 Pointer Per Game

players_avg_2PointerPerGame.sort_values(by=['2PointerPerGame_Mean'], ascending = False)[:10]

					2PointerPerGame_Mean
Player	Team	Position	Playoff	Year
Anthony Davis	NOP	PF	Y	2017-2018	10.0
		C	N	2016-2017	10.0
Russell Westbrook	HOU	PG	Y	2019-2020	10.0
Anthony Davis	NOP	C	N	2018-2019	9.0
LaMarcus Aldridge	SAS	C	Y	2017-2018	9.0
Giannis Antetokounmpo	MIL	PF	Y	2017-2018	9.0
Karl-Anthony Towns	MIN	C	N	2016-2017	9.0
Giannis Antetokounmpo	MIL	PF	Y	2018-2019	9.0
Anthony Davis	NOP	C	N	2015-2016	9.0
DeMar DeRozan	TOR	SG	Y	2016-2017	9.0

Explaination of Heat Map Analysis: Top 10 NBA Player Statistics based on Average 2 Pointer Per Game

Now, that we have narrowed our data down to top 10 NBA players having a high average of 2 pointer per game we can see that some of the players have been consistently performing well in their individual statistics shooting like Anthony Davis leading in points in the year 2017 and 2016, also being in teams that playoffs. At the same time Anthony Davis have scored an average of 9 two per games in the year 2018 and 2015 but did not make playoffs with same team.

On the other hand, there are two players for instance Karl-Anthony Towns and DeMar DeRozan who has been scoring consistently with an average of 9 two pointer per game in the same year 2016-2017 are in two different team but only one of them made the playoffs. The data indicates that overall teams performance plays a vital roles in making playoffs then just individual players statistics.

In addition, the data reveals that 4 out of 10 players in the leading two points shooting category were not in the teams that made the playoffs which is 40% of the data above. It further proves that individual players statistics do not correlate to their chances of always making the playoffs or being in the teams that make the playoffs.

Heat Analysis: On Individual NBA Player Statistics based on Average Fields Goals Per Game

We should now analyze FieldGoalsPerGame shooting category with overall players statistics to see if the data reveals similar patterns in comparison two pointer shooting category. We will use FieldGoalsPerGame score for our analysis to find top 50 players who have a high average in the fields goals shooting category. Also, include both three and two pointer shots category statistics in this column based on position of an NBA player they shoot only 2 or 3 pointers. Based on the following heat map data analysis it would help us reveal that either our hunch is correct or incorrect regarding NBA players who have been scoring an average of high fields goals per game and are more likely to be in teams that make the NBA playoffs.

Let's look at top 50 individual NBA players statistics grouped in four different categories by Team, position, playoff, year, and sorted on average of field goals per game

# look at mean of overall 
nba_fieldGoals =df.groupby(['Player','Team','Position','Playoff','Year']).agg({'FieldGoalsPerGame':['mean']})
nba_fieldGoals.columns = ['FieldGoalsPerGame_Mean']

players_avg_fieldGoalsPerGame = nba_fieldGoals.sort_values(by=['FieldGoalsPerGame_Mean'], ascending = False)[:50]
players_avg_fieldGoalsPerGame.style.background_gradient(cmap = 'coolwarm')

					FieldGoalsPerGame_Mean
Player	Team	Position	Playoff	Year
James Harden	HOU	PG	Y	2018-2019	11.000000
Russell Westbrook	HOU	PG	Y	2019-2020	11.000000
Giannis Antetokounmpo	MIL	PF	Y	2019-2020	11.000000
Kyrie Irving	BRK	PG	Y	2019-2020	10.000000
Bradley Beal	WAS	SG	N	2019-2020	10.000000
Russell Westbrook	OKC	PG	Y	2016-2017	10.000000
James Harden	HOU	SG	Y	2019-2020	10.000000
Stephen Curry	GSW	PG	Y	2015-2016	10.000000
Luka Dončić	DAL	PG	Y	2019-2020	10.000000
Karl-Anthony Towns	MIN	C	N	2016-2017	10.000000
LeBron James	LAL	SF	N	2018-2019	10.000000
		PG	Y	2019-2020	10.000000
	CLE	SF	Y	2016-2017	10.000000
		PF	Y	2017-2018	10.000000
Giannis Antetokounmpo	MIL	PF	Y	2017-2018	10.000000
				2018-2019	10.000000
LeBron James	CLE	SF	Y	2015-2016	10.000000
DeMar DeRozan	TOR	SG	Y	2016-2017	10.000000
Anthony Davis	NOP	PF	Y	2017-2018	10.000000
Kevin Durant	OKC	SF	Y	2015-2016	10.000000
Anthony Davis	NOP	C	N	2016-2017	10.000000
Kyrie Irving	BOS	PG	Y	2018-2019	9.000000
Russell Westbrook	OKC	PG	Y	2017-2018	9.000000
Isaiah Thomas	BOS	PG	Y	2016-2017	9.000000
Victor Oladipo	IND	SG	Y	2017-2018	9.000000
Russell Westbrook	OKC	PG	Y	2018-2019	9.000000
CJ McCollum	POR	SG	Y	2019-2020	9.000000
Kevin Durant	GSW	SF	Y	2017-2018	9.000000
James Harden	HOU	SG	Y	2015-2016	9.000000
Bradley Beal	WAS	SG	N	2018-2019	9.000000
Kevin Durant	GSW	SF	Y	2018-2019	9.000000
Zach LaVine	CHI	SG	N	2019-2020	9.000000
Trae Young	ATL	PG	N	2019-2020	9.000000
CJ McCollum	POR	SG	Y	2016-2017	9.000000
Damian Lillard	POR	PG	Y	2016-2017	9.000000
				2017-2018	9.000000
James Harden	HOU	SG	Y	2017-2018	9.000000
Paul George	OKC	SF	Y	2018-2019	9.000000
Kyrie Irving	CLE	PG	Y	2016-2017	9.000000
Kawhi Leonard	LAC	SF	Y	2019-2020	9.000000
LaMarcus Aldridge	SAS	C	Y	2017-2018	9.000000
Andrew Wiggins	MIN	SF	N	2016-2017	9.000000
Kyrie Irving	BOS	PG	Y	2017-2018	9.000000
Kemba Walker	CHO	PG	N	2018-2019	9.000000
Kawhi Leonard	TOR	SF	Y	2018-2019	9.000000
	SAS	SF	Y	2016-2017	9.000000
Anthony Davis	LAL	PF	Y	2019-2020	9.000000
Blake Griffin	LAC	PF	Y	2015-2016	9.000000
Damian Lillard	POR	PG	Y	2019-2020	9.000000
Anthony Davis	NOP	C	N	2015-2016	9.000000

Explaination of Heat Map Analysis: Top 50 NBA Player Statistics based on Field Goals Per Game

In the top 50 NBA player statistics 11 field goals per game is the highest average points in the field goals shooting category scored by James Harden, Russell Westbrook as they both were in same team, and Giannis Antetokounmpo was in different team that made the playoffs, and have been consistent in their performance over the years based on the data.

But if we look at the broader picture in the heat map data analysis even though there are some players like LeBron James, Kyrie Irving, and Kevin Durant that appear more than twice in the data above because either they played for different teams or their consistently performing in well in multiple position in different years scoring a high average of fields goals per game. But the main point is there are 10 out of 50 NBA player were not on teams that made the playoffs which 20% of the data.

Since, field goals column includes both players shooting 2 and 3 pointer per games it concludes that are hunch is incorrect regarding players scoring an average of high 3-pointers or 2-pointers per game and are more likely to be in teams that make the NBA playoffs because according to the field goals per games analysis above shows that players should be able to score better in both shooting category. To further prove our assumptions on this heat map analysis we will narrow the data down to top 10 NBA players based on average field goals per game.

Top 10 NBA Player Statistics based on Average Field Goals Per Game

players_avg_fieldGoalsPerGame.sort_values(by=['FieldGoalsPerGame_Mean'], ascending = False)[:10]

					FieldGoalsPerGame_Mean
Player	Team	Position	Playoff	Year
James Harden	HOU	PG	Y	2018-2019	11.0
Giannis Antetokounmpo	MIL	PF	Y	2019-2020	11.0
Russell Westbrook	HOU	PG	Y	2019-2020	11.0
LeBron James	CLE	SF	Y	2016-2017	10.0
Anthony Davis	NOP	C	N	2016-2017	10.0
Kevin Durant	OKC	SF	Y	2015-2016	10.0
Anthony Davis	NOP	PF	Y	2017-2018	10.0
LeBron James	CLE	SF	Y	2015-2016	10.0
Giannis Antetokounmpo	MIL	PF	Y	2018-2019	10.0
				2017-2018	10.0

Explaination of Heat Map Analysis: Top 10 NBA Player Statistics based on Average Field Goals Per Game

Now, that we have narrowed our data down to top 10 NBA players having a high average of field goals per game we can see that some of the players have been consistently performing well in their individual statistics shooting like Giannis Antetokounmpo leading in points in the year 2019 and 2018, also being in same teams that made playoffs. Similarly, James Harden and Russell Westbrookhave tied shooting average of 11 fields goals per game on same team.

On the other hand, there are players for instance Anthony Davis who has been scoring consistently with 10 field goals per game in the year '2016' and '2017' with same team but made the playoffs only once. In addition, the data reveals that 1 out of 10 NBA players in the above heat map analysis were not on teams that made the playoffs which is 10% of the data above. It further proves that individual players statistics in the fields goals per game category do correlate to their chances of always making the playoffs or being in the teams that make the playoffs.

Heat Analysis: On Individual NBA Player Statistics based on Average Total Points Per Game

We should now analyze TotalPointsPerGame shooting category with overall players statistics to see if the data reveals similar patterns in comparison field goals category. We will use TotalPointsPerGame score for our analysis to find top 50 players who have a high average in the total points per game category. Based on the following heat map data analysis it would help us reveal that either our hunch is correct or incorrect regarding NBA players who have been scoring an average of high total points per game and are more likely to be in teams that make the NBA playoffs.

Let's look at top 50 individual NBA players statistics grouped in four different categories by Team, position, playoff, year, and sorted on average of total points per game

# look at mean of overall 3 Pointers  and the number of reviews
nba_totalpoints =df.groupby(['Player', 'Team','Playoff','Position','Year']).agg({'TotalPointsPerGame':['mean']})
nba_totalpoints.columns = ['TotalPointsPerGame_Mean']

players_avg_totalpointsPerGame = nba_totalpoints.sort_values(by=['TotalPointsPerGame_Mean'],ascending = False)[:50]
players_avg_totalpointsPerGame.style.background_gradient(cmap = 'CMRmap')

					TotalPointsPerGame_Mean
Player	Team	Playoff	Position	Year
James Harden	HOU	Y	PG	2018-2019	36.000000
			SG	2019-2020	34.000000
Russell Westbrook	OKC	Y	PG	2016-2017	32.000000
Bradley Beal	WAS	N	SG	2019-2020	31.000000
Trae Young	ATL	N	PG	2019-2020	30.000000
Stephen Curry	GSW	Y	PG	2015-2016	30.000000
Damian Lillard	POR	Y	PG	2019-2020	30.000000
James Harden	HOU	Y	SG	2017-2018	30.000000
Isaiah Thomas	BOS	Y	PG	2016-2017	29.000000
Luka Dončić	DAL	Y	PG	2019-2020	29.000000
James Harden	HOU	Y	SG	2015-2016	29.000000
Giannis Antetokounmpo	MIL	Y	PF	2019-2020	29.000000
James Harden	HOU	Y	PG	2016-2017	29.000000
Paul George	OKC	Y	SF	2018-2019	28.000000
Kevin Durant	OKC	Y	SF	2015-2016	28.000000
Joel Embiid	PHI	Y	C	2018-2019	28.000000
Anthony Davis	NOP	N	C	2016-2017	28.000000
DeMarcus Cousins	SAC	N	C	2016-2017	28.000000
Anthony Davis	NOP	Y	PF	2017-2018	28.000000
Giannis Antetokounmpo	MIL	Y	PF	2018-2019	28.000000
				2017-2018	27.000000
DeMarcus Cousins	TOT	N	C	2016-2017	27.000000
Kawhi Leonard	LAC	Y	SF	2019-2020	27.000000
LeBron James	CLE	Y	PF	2017-2018	27.000000
Stephen Curry	GSW	Y	PG	2018-2019	27.000000
DeMarcus Cousins	SAC	N	C	2015-2016	27.000000
Damian Lillard	POR	Y	PG	2016-2017	27.000000
				2017-2018	27.000000
DeMar DeRozan	TOR	Y	SG	2016-2017	27.000000
Kawhi Leonard	TOR	Y	SF	2018-2019	27.000000
Kyrie Irving	BRK	Y	PG	2019-2020	27.000000
LeBron James	LAL	N	SF	2018-2019	27.000000
Devin Booker	PHO	N	SG	2018-2019	27.000000
Russell Westbrook	HOU	Y	PG	2019-2020	27.000000
Devin Booker	PHO	N	SG	2019-2020	27.000000
LeBron James	CLE	Y	SF	2016-2017	26.000000
Kevin Durant	GSW	Y	SF	2017-2018	26.000000
Kemba Walker	CHO	N	PG	2018-2019	26.000000
Stephen Curry	GSW	Y	PG	2017-2018	26.000000
Damian Lillard	POR	Y	PG	2018-2019	26.000000
Anthony Davis	NOP	N	C	2018-2019	26.000000
Bradley Beal	WAS	N	SG	2018-2019	26.000000
Anthony Davis	LAL	Y	PF	2019-2020	26.000000
Karl-Anthony Towns	MIN	N	C	2019-2020	26.000000
Zach LaVine	CHI	N	SG	2019-2020	26.000000
Kawhi Leonard	SAS	Y	SF	2016-2017	26.000000
Kevin Durant	GSW	Y	SF	2018-2019	26.000000
LeBron James	CLE	Y	SF	2015-2016	25.000000
Karl-Anthony Towns	MIN	N	C	2016-2017	25.000000
Devin Booker	PHO	N	SG	2017-2018	25.000000

Explaination of Heat Map Analysis: Top 50 NBA Player Statistics based on Total Points Per Game

In the top 50 NBA player statistics 36 total points per game is the highest average in the total points category scored by James Harden, and Russell Westbrook scored an average of 32 total points per game as they both were in different teams that made the playoffs, and have been consistent in their performance over the years based on the data. But at the same time Bradley Beal amd Trae Young were also top 4 leading scorers in the total points category in 2019-2020 season but their teams did not make the playoffs. It shows that having good performance in the total points per game category does not guarantee playoffs spot.

But if we look at the broader picture in the heat map data analysis even though there are some players like LeBron James, Anthony Davis, Bradley Bealand Kevin Durant that appear more than twice in the data above because either they played for different teams or their consistently performing in well in multiple position in different years scoring a high average of total points per games. But the main point is there are 16 out of 50 NBA player were not in teams that made the playoffs which 32% of the data. It concludes that are hunch is correct regarding the total points per games analysis above shows that players should be able to score better in both shooting category. To further prove our assumptions on this heat map analysis we will narrow the data down to top 10 NBA players based on average total points per game

Top 10 NBA Player Statistics based on Average Total Points Goals Per Game

players_avg_totalpointsPerGame.sort_values(by=['TotalPointsPerGame_Mean'], ascending = False)[:10]

					TotalPointsPerGame_Mean
Player	Team	Playoff	Position	Year
James Harden	HOU	Y	PG	2018-2019	36.0
			SG	2019-2020	34.0
Russell Westbrook	OKC	Y	PG	2016-2017	32.0
Bradley Beal	WAS	N	SG	2019-2020	31.0
Trae Young	ATL	N	PG	2019-2020	30.0
Stephen Curry	GSW	Y	PG	2015-2016	30.0
Damian Lillard	POR	Y	PG	2019-2020	30.0
James Harden	HOU	Y	SG	2017-2018	30.0
Isaiah Thomas	BOS	Y	PG	2016-2017	29.0
Luka Dončić	DAL	Y	PG	2019-2020	29.0

Explaination of Heat Map Analysis: Top 10 NBA Player Statistics based on Average Total Points Per Game

Now, that we have narrowed our data down to top 10 NBA players having a high average of total points per game we can see that some of the players have been consistently performing well in their individual statistics like James Harden leading in points in the year 2019 and 2018, also being in same teams that made playoffs. Similarly, Russell Westbrookhave scored an average of 32 total points per game being in different team that made playoffs.

On the other hand, there are players like Bradley Beal who have scored 31 total points per game and Trae Young have scored 30 total points per game in the same regular season 2019-2020 with different team but did not make the playoffs. In addition, the data reveals that 2 out of 10 players from the above data were not on teams that made the playoffs which is 20% of the data above. It further proves that individual players statistics in the total points per game category do correlate to their chances of always making the playoffs but it is also dependent on the overall team statistics.

Project Findings

Based on the visualization data revealed that scoring a high average in shooting category does not necessarily guarantee a spot in the playoffs there are other categories in the statistics that also play vital role to achieve that goal. For instance, hypothesis 1 was partially supported as shown in the swarm plot and pie chart that some position like Shooting Guard (SG) and Center (C) have scored higher three and two pointer per game which was correct based on our hunch. But the other NBA players positions have been fluctuating because players might be trying to learn how to improve in both three and two pointer shooting category over the years which led to us seeing tie and close percentages

The hypothesis 2 was partially supported NBA players who have played around range of 80 games and have an average of greater than 30 minutes per game have more chances of making playoffs, and scoring more points. The overall analysis shows light position correlation between GamesPlayed and MinutesPerGame column as they have been categorized based on playoffs. Assumptions made in hypothesis 3 were partially supported as well because the data revealed that 4 out of 10 players in leading average three and two pointer shooting category were not in the teams that made the playoffs which is 40% of the data. It further proves that based on the individual players statistics shows neutral correlation with shooting category to their chances of making the playoffs because its dependent on the overall team’s statistics data.

I did not encounter difficulties with the data, but it would be interesting to gather overall team’s statistics data from 2015 to 2020 and merge with current dataset of player’s playoffs statistics data so that we can do analysis on other various categories which would lead to seeing better correlations between team and players columns. Also, how it affects their chances of making playoffs that would possibly improve the outcomes of the analysis. To take it a step further I would look into analyzing the dynamics of each NBA players positions as well because the players are improving in both three and two pointer shooting category over the years.